BranchesTags

Repository files navigation

Enabling Viewpoint Learning through Dynamic Label Generation

Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.

M. Schelling, P. Hermosilla, P.-P. Vázquez and T. Ropinski

Teaser

Paper Pre-Print

Project Page

Prerequisites

  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder

This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7. For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary. We recommend to run training in a tf=1.11_gpu docker container.

Example Training

The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).

Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).

Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.

View Quality computation

The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.

A basic computation can be done via:

pythonDataProcessing.py--generate_views--fDATA_DIR

which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.

About

No description, website, or topics provided.

Resources

Readme
License
Activity

Stars

1 star

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} 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
Enabling Viewpoint Learning through Dynamic Label Generation
Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.
M. Schelling,
P. Hermosilla,
P.-P. Vázquez
and T. Ropinski
Teaser
Paper Pre-Print
Project Page
Prerequisites
  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder
This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7.
For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary.
We recommend to run training in a tf=1.11_gpu docker container.
Example Training
The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).
Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).
Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.
View Quality computation
The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.
A basic computation can be done via:
pythonDataProcessing.py--generate_views--fDATA_DIR
which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
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
Enabling Viewpoint Learning through Dynamic Label Generation
Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.
M. Schelling,
P. Hermosilla,
P.-P. Vázquez
and T. Ropinski
Teaser
Paper Pre-Print
Project Page
Prerequisites
  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder
This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7.
For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary.
We recommend to run training in a tf=1.11_gpu docker container.
Example Training
The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).
Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).
Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.
View Quality computation
The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.
A basic computation can be done via:
pythonDataProcessing.py--generate_views--fDATA_DIR
which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
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
Enabling Viewpoint Learning through Dynamic Label Generation
Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.
M. Schelling,
P. Hermosilla,
P.-P. Vázquez
and T. Ropinski
Teaser
Paper Pre-Print
Project Page
Prerequisites
  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder
This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7.
For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary.
We recommend to run training in a tf=1.11_gpu docker container.
Example Training
The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).
Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).
Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.
View Quality computation
The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.
A basic computation can be done via:
pythonDataProcessing.py--generate_views--fDATA_DIR
which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
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
Enabling Viewpoint Learning through Dynamic Label Generation
Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.
M. Schelling,
P. Hermosilla,
P.-P. Vázquez
and T. Ropinski
Teaser
Paper Pre-Print
Project Page
Prerequisites
  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder
This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7.
For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary.
We recommend to run training in a tf=1.11_gpu docker container.
Example Training
The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).
Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).
Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.
View Quality computation
The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.
A basic computation can be done via:
pythonDataProcessing.py--generate_views--fDATA_DIR
which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
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
Enabling Viewpoint Learning through Dynamic Label Generation
Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.
M. Schelling,
P. Hermosilla,
P.-P. Vázquez
and T. Ropinski
Teaser
Paper Pre-Print
Project Page
Prerequisites
  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder
This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7.
For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary.
We recommend to run training in a tf=1.11_gpu docker container.
Example Training
The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).
Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).
Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.
View Quality computation
The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.
A basic computation can be done via:
pythonDataProcessing.py--generate_views--fDATA_DIR
which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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
Enabling Viewpoint Learning through Dynamic Label Generation
Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.
M. Schelling,
P. Hermosilla,
P.-P. Vázquez
and T. Ropinski
Teaser
Paper Pre-Print
Project Page
Prerequisites
  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder
This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7.
For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary.
We recommend to run training in a tf=1.11_gpu docker container.
Example Training
The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).
Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).
Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.
View Quality computation
The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.
A basic computation can be done via:
pythonDataProcessing.py--generate_views--fDATA_DIR
which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
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
Enabling Viewpoint Learning through Dynamic Label Generation
Tensorflow implementation of Enabling Viewpoint Learning through Dynamic Label Generation, published at Eurographics 2021.
M. Schelling,
P. Hermosilla,
P.-P. Vázquez
and T. Ropinski
Teaser
Paper Pre-Print
Project Page
Prerequisites
  • Download and compile the MCCNN library and place it it the MCCNN/ folder.
  • Download the data and place it in the viewpoint_learning/data/ folder
This Implementation is in TensorFlow 1 and was tested using TF 1.11 and Python 2.7.
For the viewpoint computation methods the OpenGL package for python is required. For training this is not necessary.
We recommend to run training in a tf=1.11_gpu docker container.
Example Training
The root directory contains scripts to train viewpoint prediction networks using dynamic label generation with Multiple Labels (ML), Gaussian Labels (GL) and a two staged learning using both (ML-GL).
Reference implementation are given for Single Label (SL), Spherical Regression (SR), Deep Label Distribution Learning (DLDL).
Note: By default this only trains the airplane category, to train other categories in parallel on multiple GPUs please uncomment the respective lines in the script_*.sh files.
View Quality computation
The function to compute view qualites from meshes are in viewpoint_learning/code/DataProcessing.py.
A basic computation can be done via:
pythonDataProcessing.py--generate_views--fDATA_DIR
which computes Visibility Ratio, Viewpoint Entropy, Viewpoint Kullback-Leibler Distance and Viewpoint Mutual Information.
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages