ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields
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
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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)
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