BranchesTags

Repository files navigation

Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression

Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.

News

  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).

Requirments

Authors

These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.

About

Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression

Resources

Readme
License
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
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
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale
correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.
News
  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).
Requirments
Authors
These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.
About
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Resources
Readme
License
Activity
Stars
0 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
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale
correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.
News
  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).
Requirments
Authors
These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.
About
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Resources
Readme
License
Activity
Stars
0 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
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale
correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.
News
  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).
Requirments
Authors
These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.
About
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Resources
Readme
License
Activity
Stars
0 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
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale
correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.
News
  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).
Requirments
Authors
These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.
About
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Resources
Readme
License
Activity
Stars
0 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
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale
correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.
News
  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).
Requirments
Authors
These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.
About
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
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
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale
correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.
News
  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).
Requirments
Authors
These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.
About
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Resources
Readme
License
Activity
Stars
0 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
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Abstract—This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor based voxelized PCG, dubbed as the SparsePCGC. The proposed SparsePCGC is an ultra lightweight solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). And the multiscale representation allows us to compress scale-wise MP-POVs by extensively and flexibly exploiting cross-scale and same-scale
correlations. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV, e.g., pMP-POV. Thus, we first design the Sparse Convolution based Neural Networks (SparseCNN) that stack sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop SparseCNN based Occupancy Probability Approximation (SOPA) model to estimate the pMP-POVs either in a single-stage manner only using the cross-scale correlation or in a multi-stage means by stage-wisely exploiting correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial prior in feature attribute to improve the SOPA. Our unified approach not only shows state-of-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based compression schemes, but also presents lightweight complexity consumption which is attractive to practical applications.
News
  • 2022.09.09 Upload supplementary material, which includes comparison details. see Supplementary_Material.pdf
  • 2022.06.16 Source codes will be released soon to the public after the approval from the funding agency. Now We make the testing results, testing conditions, pretrained models, and other relevant materials publicly accessible.
  • 2022.06.16 We simplify the implementation and reduce the computational complexity significantly. (e.g., almost 6∼8×). At the same time, we slightly adjust model parameters and achieve better performance on sparse LiDAR point clouds.
  • 2022.01.13 We participate in MPEG AI-3DGC (EE. 13.54).
  • 2021.11.23 We have posted the manuscript on arxiv (https://arxiv.org/abs/2111.10633).
Requirments
Authors
These files are provided by Nanjing University Vision Lab. Thanks for the help of Prof. Dandan Ding from Hangzhou Normal University, Prof. Zhu Li from University of Missouri at Kansas. Please contact us (wangjq@smail.nju.edu.cn and mazhan@nju.edu.cn) if you have any questions.
About
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages