待翻譯：ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.27735v1 Announce Type: new Abstract: We present ABCD (Alpha-Composited Block Coordinate Descent), an out-of-core training framework for alpha-composited radiance fields, instantiated here for 3D Gaussian Splatting. Our method reformulates training as block coordinate descent over spatial partitions: only one block of parameters is active at a time, while all others are frozen. By exploiting the associativity of alpha blending, these inactive regions can be pre-rendered and collapsed into foreground and background RGBA images. As a result, for fixed partition size and image resolution, peak VRAM becomes O(1) with respect to total scene extent, rather than growing with full scene size. This enables GPUs with limited memory to train scenes that would otherwise not fit in core. In experiments, our method closely preserves the reconstruction quality of 3DGS, with less than 5% PSNR degradation, while ABCD with compositing ablated suffers roughly 40% degradation. Our code can be found at https://github.com/shiukaheng/abcd
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--> [Submitted on 27 Aug 2026] Title:ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields View a PDF of the paper titled ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields, by Ka Heng Shiu and Kartic Subr View PDF HTML (experimental) Abstract:We present ABCD (Alpha-Composited Block Coordinate Descent), an out-of-core training framework for alpha-composited radiance fields, instantiated here for 3D Gaussian Splatting. Our method reformulates training as block coordinate descent over spatial partitions: only one block of parameters is active at a time, while all others are frozen. By exploiting the associativity of alpha blending, these inactive regions can be pre-rendered and collapsed into foreground and background RGBA images. As a result, for fixed partition size and image resolution, peak VRAM becomes O(1) with respect to total scene extent, rather than growing with full scene size. This enables GPUs with limited memory to train scenes that would otherwise not fit in core. In experiments, our method closely preserves the reconstruction quality of 3DGS, with less than 5% PSNR degradation, while ABCD with compositing ablated suffers roughly 40% degradation. Our code can be found at this https URL Comments: Presented at ACM SIGGRAPH 2026 Posters Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) ACM classes: I.3.7 Cite as: arXiv:2608.27735 [cs.CV] (or arXiv:2608.27735v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.27735 arXiv-issued DOI via DataCite (pending registration) Journal reference: ACM SIGGRAPH 2026 Posters (SIGGRAPH Posters '26), Article 62, 3 pages, 2026 Related DOI: https://doi.org/10.1145/3799825.3818779 DOI(s) linking to related resources Submission history From: Ka Heng Shiu Mr [view email] [v1] Thu, 27 Aug 2026 21:51:16 UTC (3,721 KB) Full-text links: Access Paper: View a PDF of the paper titled ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields, by Ka Heng Shiu and Kartic Subr View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs cs.GR References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)