GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525

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GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
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@schellmi42schellmi42 commented Mar 10, 2021•
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As requested by @G4G , I split down the pull request #453 into smaller patches.

This is the first one.

  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)

Note:

The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

This was referenced Mar 11, 2021
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@schellmi42schellmi42 mentioned this pull request May 3, 2021
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This was referenced May 20, 2021
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Open
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@G4G
G4G self-requested a review June 7, 2021 10:26
G4G
G4G approved these changes Jun 16, 2021
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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" + '

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GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525
Open
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Open
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
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@schellmi42schellmi42
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Mar 10, 2021•

edited

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As requested by @G4G , I split down the pull request #453 into smaller patches.
This is the first one.
  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)
Note:
The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

This was referenced Mar 11, 2021
 Open

 Open

 Open

 Open

@schellmi42schellmi42
mentioned this pull request
May 3, 2021
 Open


This was referenced May 20, 2021
 Open

 Open

 Open

 Open

 Open

 Open

@G4G
G4G
self-requested a review
June 7, 2021 10:26
G4G
G4G
approved these changes
Jun 16, 2021
View reviewed changes
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, '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
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525
Open
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Open
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
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@schellmi42
@schellmi42schellmi42
commented
Mar 10, 2021•

edited

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As requested by @G4G , I split down the pull request #453 into smaller patches.
This is the first one.
  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)
Note:
The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

This was referenced Mar 11, 2021
 Open

 Open

 Open

 Open

@schellmi42schellmi42
mentioned this pull request
May 3, 2021
 Open


This was referenced May 20, 2021
 Open

 Open

 Open

 Open

 Open

 Open

@G4G
G4G
self-requested a review
June 7, 2021 10:26
G4G
G4G
approved these changes
Jun 16, 2021
View reviewed changes
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, '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
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525
Open
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Open
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
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@schellmi42
@schellmi42schellmi42
commented
Mar 10, 2021•

edited

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As requested by @G4G , I split down the pull request #453 into smaller patches.
This is the first one.
  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)
Note:
The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

This was referenced Mar 11, 2021
 Open

 Open

 Open

 Open

@schellmi42schellmi42
mentioned this pull request
May 3, 2021
 Open


This was referenced May 20, 2021
 Open

 Open

 Open

 Open

 Open

 Open

@G4G
G4G
self-requested a review
June 7, 2021 10:26
G4G
G4G
approved these changes
Jun 16, 2021
View reviewed changes
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, '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
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525
Open
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Open
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
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@schellmi42
@schellmi42schellmi42
commented
Mar 10, 2021•

edited

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As requested by @G4G , I split down the pull request #453 into smaller patches.
This is the first one.
  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)
Note:
The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

This was referenced Mar 11, 2021
 Open

 Open

 Open

 Open

@schellmi42schellmi42
mentioned this pull request
May 3, 2021
 Open


This was referenced May 20, 2021
 Open

 Open

 Open

 Open

 Open

 Open

@G4G
G4G
self-requested a review
June 7, 2021 10:26
G4G
G4G
approved these changes
Jun 16, 2021
View reviewed changes
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2 participants

@schellmi42@G4G

, '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
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525
Open
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Open
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Conversation
@schellmi42
@schellmi42schellmi42
commented
Mar 10, 2021•

edited

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As requested by @G4G , I split down the pull request #453 into smaller patches.
This is the first one.
  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)
Note:
The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

This was referenced Mar 11, 2021
 Open

 Open

 Open

 Open

@schellmi42schellmi42
mentioned this pull request
May 3, 2021
 Open


This was referenced May 20, 2021
 Open

 Open

 Open

 Open

 Open

 Open

@G4G
G4G
self-requested a review
June 7, 2021 10:26
G4G
G4G
approved these changes
Jun 16, 2021
View reviewed changes
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@G4GG4GG4G approved these changes
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2 participants

@schellmi42@G4G

, '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
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525
Open
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Open
GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
Conversation
@schellmi42
@schellmi42schellmi42
commented
Mar 10, 2021•

edited

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As requested by @G4G , I split down the pull request #453 into smaller patches.
This is the first one.
  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)
Note:
The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

This was referenced Mar 11, 2021
 Open

 Open

 Open

 Open

@schellmi42schellmi42
mentioned this pull request
May 3, 2021
 Open


This was referenced May 20, 2021
 Open

 Open

 Open

 Open

 Open

 Open

@G4G
G4G
self-requested a review
June 7, 2021 10:26
G4G
G4G
approved these changes
Jun 16, 2021
View reviewed changes
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@G4GG4GG4G approved these changes
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2 participants

@schellmi42@G4G

, '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); }
})();
})();

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GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12) - #525
Open
schellmi42 wants to merge 8 commits into
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schellmi42:GSoC.point_cloud_class
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GSoC: Incorporating point-based convolutions in TensorFlow Graphics (1/12)#525
schellmi42 wants to merge 8 commits into
tensorflow:masterfrom 
schellmi42:GSoC.point_cloud_class
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@schellmi42
@schellmi42schellmi42
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Mar 10, 2021•

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As requested by @G4G , I split down the pull request #453 into smaller patches.
This is the first one.
  1. point cloud class
  2. regular grid computation
  3. neighbor computation (ball query)
  4. point density estimation
  5. spatial sampling (poisson disk and cell-average)
  6. point hierarchy class
  7. 1x1 convolution and pooling layers
  8. MCConv layer (incl. Monte-Carlo integration approximation method)
  9. PointConv layer
  10. KPConv layer (incl. constant summation approximation method and kernel point initialization)
  11. example Colab-Notebooks
  12. custom-ops package (CUDA-kernels)
Note:
The code is slightly different from the first PR #453. It's now possible to execute all functions in graph mode.

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@schellmi42schellmi42
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May 3, 2021
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@G4G
G4G
self-requested a review
June 7, 2021 10:26
G4G
G4G
approved these changes
Jun 16, 2021
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