Technology Category
Computer Vision: 3D Computer VisionMachine Learning: Scalability of ML SystemsSearch and Optimization: Sampling/Simulation-based Search
AdaGScale: Viewpoint-Adaptive Gaussian Scaling in 3D Gaussian Splatting to Reduce Gaussian-Tile Pairs
🤖 AI Summary
This work addresses the inefficiency in GPU rendering caused by an excessive number of Gaussian–tile pairs in 3D Gaussian Splatting (3D-GS). To mitigate this issue, the authors propose a viewpoint-adaptive Gaussian scaling method that dynamically adjusts the spatial extent of each Gaussian during tile intersection tests based on its estimated color contribution in image boundary regions, computed in a preprocessing step. Crucially, the scaled size is used only for intersection culling, while color accumulation retains the original Gaussian parameters, thereby preserving visual fidelity. This approach introduces, for the first time, the notion of importance-aware Gaussian–tile pair selection to enhance rendering efficiency. Experiments demonstrate an average speedup of 13.8× on urban scenes with a negligible PSNR degradation of approximately 0.5 dB.
Technology Category
Application Category
📝 Abstract
Reducing the number of Gaussian-tile pairs is one of the most promising approaches to improve 3D Gaussian Splatting (3D-GS) rendering speed on GPUs. However, the importance difference existing among Gaussian-tile pairs has never been considered in the previous works. In this paper, we propose AdaGScale, a novel viewpoint-adaptive Gaussian scaling technique for reducing the number of Gaussian-tile pairs. AdaGScale is based on the observation that the peripheral tiles located far from Gaussian center contribute negligibly to pixel color accumulation. This suggests an opportunity for reducing the number of Gaussian-tile pairs based on color contribution. AdaGScale efficiently estimates the color contribution in the peripheral region of each Gaussian during a preprocessing stage and adaptively scales its size based on the peripheral score. As a result, Gaussians with lower importance intersect with fewer tiles during the intersection test, which improves rendering speed while maintaining image quality. The adjusted size is used only for tile intersection test, and the original size is retained during color accumulation to preserve visual fidelity. Experimental results show that AdaGScale achieves a geometric mean speedup of 13.8x over original 3D-GS on a GPU, with only about 0.5 dB degradation in PSNR on city-scale scenes.
Problem
Research questions and friction points this paper is trying to address.
3D Gaussian Splatting
Gaussian-tile pairs
rendering acceleration
viewpoint adaptation
color contribution
Innovation
Methods, ideas, or system contributions that make the work stand out.
viewpoint-adaptive
Gaussian scaling
Gaussian-tile pairs reduction
3D Gaussian Splatting
color contribution estimation
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