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360-visualization

IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.

We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.

We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.

Fig. 1: Objects and reachable viewpoints visualization

Fig. 2: Crafted instruction generated with retrieved objects and viewpoints

Authors:

Repository distribution

├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation

If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.

We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator and all requirements for visualizing.

Some advices:

  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.

As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.

For parsing and using house metadata, see the metadata parser docs.

Run

You can run the notebooks with jupyter notebook command.

Please this guide for use of the object visualization.

(IA Lab only) Run on remote host

For local forwarding on remote server use

ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.

If you are connecting to IA LAB Cluster, use:

sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.

and cancel after not use

scancel <process ID SLURM> # you can review the ID with `sq`

Contributing

Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)

About

AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator

Resources

Readme
MIT license
Activity

Stars

5 stars

Watchers

0 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
360-visualization
IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.
We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.
We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.
Fig. 1: Objects and reachable viewpoints visualization
Fig. 2: Crafted instruction generated with retrieved objects and viewpoints
Authors:
Repository distribution
├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation
If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.
We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator
and all requirements for visualizing.
Some advices:
  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.
As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.
For parsing and using house metadata, see the metadata parser docs.
Run
You can run the notebooks with jupyter notebook command.
Please this guide for use of the object visualization.
(IA Lab only) Run on remote host
For local forwarding on remote server use
ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.
If you are connecting to IA LAB Cluster, use:
sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.
and cancel after not use
scancel <process ID SLURM> # you can review the ID with `sq`

Contributing
Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)
About
AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator
Resources
Readme
MIT license
Activity
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
360-visualization
IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.
We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.
We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.
Fig. 1: Objects and reachable viewpoints visualization
Fig. 2: Crafted instruction generated with retrieved objects and viewpoints
Authors:
Repository distribution
├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation
If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.
We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator
and all requirements for visualizing.
Some advices:
  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.
As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.
For parsing and using house metadata, see the metadata parser docs.
Run
You can run the notebooks with jupyter notebook command.
Please this guide for use of the object visualization.
(IA Lab only) Run on remote host
For local forwarding on remote server use
ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.
If you are connecting to IA LAB Cluster, use:
sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.
and cancel after not use
scancel <process ID SLURM> # you can review the ID with `sq`

Contributing
Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)
About
AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator
Resources
Readme
MIT license
Activity
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
360-visualization
IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.
We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.
We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.
Fig. 1: Objects and reachable viewpoints visualization
Fig. 2: Crafted instruction generated with retrieved objects and viewpoints
Authors:
Repository distribution
├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation
If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.
We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator
and all requirements for visualizing.
Some advices:
  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.
As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.
For parsing and using house metadata, see the metadata parser docs.
Run
You can run the notebooks with jupyter notebook command.
Please this guide for use of the object visualization.
(IA Lab only) Run on remote host
For local forwarding on remote server use
ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.
If you are connecting to IA LAB Cluster, use:
sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.
and cancel after not use
scancel <process ID SLURM> # you can review the ID with `sq`

Contributing
Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)
About
AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator
Resources
Readme
MIT license
Activity
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
360-visualization
IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.
We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.
We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.
Fig. 1: Objects and reachable viewpoints visualization
Fig. 2: Crafted instruction generated with retrieved objects and viewpoints
Authors:
Repository distribution
├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation
If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.
We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator
and all requirements for visualizing.
Some advices:
  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.
As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.
For parsing and using house metadata, see the metadata parser docs.
Run
You can run the notebooks with jupyter notebook command.
Please this guide for use of the object visualization.
(IA Lab only) Run on remote host
For local forwarding on remote server use
ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.
If you are connecting to IA LAB Cluster, use:
sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.
and cancel after not use
scancel <process ID SLURM> # you can review the ID with `sq`

Contributing
Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)
About
AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator
Resources
Readme
MIT license
Activity
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
360-visualization
IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.
We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.
We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.
Fig. 1: Objects and reachable viewpoints visualization
Fig. 2: Crafted instruction generated with retrieved objects and viewpoints
Authors:
Repository distribution
├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation
If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.
We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator
and all requirements for visualizing.
Some advices:
  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.
As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.
For parsing and using house metadata, see the metadata parser docs.
Run
You can run the notebooks with jupyter notebook command.
Please this guide for use of the object visualization.
(IA Lab only) Run on remote host
For local forwarding on remote server use
ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.
If you are connecting to IA LAB Cluster, use:
sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.
and cancel after not use
scancel <process ID SLURM> # you can review the ID with `sq`

Contributing
Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)
About
AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator
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Readme
MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
360-visualization
IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.
We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.
We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.
Fig. 1: Objects and reachable viewpoints visualization
Fig. 2: Crafted instruction generated with retrieved objects and viewpoints
Authors:
Repository distribution
├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation
If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.
We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator
and all requirements for visualizing.
Some advices:
  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.
As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.
For parsing and using house metadata, see the metadata parser docs.
Run
You can run the notebooks with jupyter notebook command.
Please this guide for use of the object visualization.
(IA Lab only) Run on remote host
For local forwarding on remote server use
ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.
If you are connecting to IA LAB Cluster, use:
sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.
and cancel after not use
scancel <process ID SLURM> # you can review the ID with `sq`

Contributing
Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)
About
AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator
Resources
Readme
MIT license
Activity
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
360-visualization
IA LAB PUC Research on VLN task, visualization fo 360° indoor environments on Matterport simulator.
We take the Matterport metadata and make it readily available to enhance tasks built on this dataset, such as the Visual and Language Navigation (VLN) task.
We also create functions for getting new crafted instructions (instructions based on rules with the best objects and viewpoints from the environment recommended for the agent to navigate) and the function for extracting these objects.
Fig. 1: Objects and reachable viewpoints visualization
Fig. 2: Crafted instruction generated with retrieved objects and viewpoints
Authors:
Repository distribution
├── LICENSE
├── README.md
├── docs/
├── img/
├── connectivity/ 👉🏽 Folder containing connectivity graphs of each scan/house
├── metadata_parser/
│ ├── house_cache/ 👉🏽 Folder containing serialized house metadata (with Pickle)
│ │ ├── 17DRP5sb8fy.pickle
│ │ ├── 1LXtFkjw3qL.pickle
│ │ ├── {...}.pickle
│ └── parse_house_segmentations.py 👉🏽 Script for generating house metadata pickles
└── slurm_scripts/ 👉🏽 Scripts for running repository scripts on Slurm (specifically on IA Lab Cluster)
├── jupyter_on_slurm.sh
└── python_on_slurm.sh
├── data_explorer.ipynb 👉🏽 Explore the metadata of Matterport simulator, by house and viewpoint nodes.
├── simulator_visualization.ipynb 👉🏽 Jupyter Notebook to visualize objects coordinates and reachable viewpoints on 360° views, from Simulator
├── crafted_instructions_generator.ipynb 👉🏽 Jupyter Notebook to generate crafted instructions and visualize with panoramas
├── utils.py 👉🏽 Python file with common functions for visualizing and computing graph related stuff
├── objects_and_crafted_instructions_utils.py 👉🏽 Python file with functions for ranking recommended objects and generate crafted instructions
├── requirements.txt 👉🏽 All Python libraries for running the project

Installation
If you are member of the IA Lab (PUC), it is already installed on the servers, so the next steps are not necessary unless you would like to run on local.
We recommend following @peteanderson80R2R paper guide for a successfully installation of Matterport simulator
and all requirements for visualizing.
Some advices:
  • Strongly recommended to follow the Docker setup.
  • When running cmake, use cmake -DEGL_RENDERING=ON .., because we will use downloaded images. Also, this allows for off-screen rendering in the GPU.
As it says, you also will need to download the dataset. For object visualization you will need to follow this guide.
For parsing and using house metadata, see the metadata parser docs.
Run
You can run the notebooks with jupyter notebook command.
Please this guide for use of the object visualization.
(IA Lab only) Run on remote host
For local forwarding on remote server use
ssh -L localhost:8889:scylla:{PORT} <user>@<host>

where PORT is the port exposed by running the Jupyter Notebook.
If you are connecting to IA LAB Cluster, use:
sbatch /home/mrearle/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh
# or
sbatch /home/jiossandon/repos/360-visualization/slurm_scripts/jupyter_on_slurm_gpu.sh

you can see the exposed port by using cat {PATH}.
and cancel after not use
scancel <process ID SLURM> # you can review the ID with `sq`

Contributing
Please contact us if you want to contribute, use objects and crafted instructions to improve a vision-language model or anything else :)
About
AI LAB PUC Research on Visual-and-language navigation task. Visualization of 360° indoor objects on Matterport simulator
Resources
Readme
MIT license
Activity
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages