Technology Category
Computer Vision: 3D Computer VisionSearch and Optimization: Distributed SearchKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
GS-TG: 3D Gaussian Splatting Accelerator with Tile Grouping for Reducing Redundant Sorting while Preserving Rasterization Efficiency
🤖 AI Summary
3D Gaussian Splatting (3D-GS) faces a fundamental trade-off between sorting and rasterization in real-time rendering: increasing tile size reduces redundant sorting but incurs extra rasterization overhead. This paper proposes a tile-grouping acceleration framework to resolve this dilemma. Our method introduces three key innovations: (1) dynamic merging of small tiles during the sorting stage to reduce sorting complexity; (2) a bitmask-based identification mechanism in rasterization to preserve fine-grained tile efficiency while eliminating redundant computations; and (3) an efficient sorting-sharing strategy enabling cross-tile reuse of sorting results. The framework is fully compatible with the original 3D-GS pipeline—requiring no model retraining and guaranteeing lossless rendering quality. Experiments demonstrate an average 1.54× speedup over the state-of-the-art acceleration methods, significantly enhancing real-time rendering performance.
Technology Category
Application Category
📝 Abstract
3D Gaussian Splatting (3D-GS) has emerged as a promising alternative to neural radiance fields (NeRF) as it offers high speed as well as high image quality in novel view synthesis. Despite these advancements, 3D-GS still struggles to meet the frames per second (FPS) demands of real-time applications. In this paper, we introduce GS-TG, a tile-grouping-based accelerator that enhances 3D-GS rendering speed by reducing redundant sorting operations and preserving rasterization efficiency. GS-TG addresses a critical trade-off issue in 3D-GS rendering: increasing the tile size effectively reduces redundant sorting operations, but it concurrently increases unnecessary rasterization computations. So, during sorting of the proposed approach, GS-TG groups small tiles (for making large tiles) to share sorting operations across tiles within each group, significantly reducing redundant computations. During rasterization, a bitmask assigned to each Gaussian identifies relevant small tiles, to enable efficient sharing of sorting results. Consequently, GS-TG enables sorting to be performed as if a large tile size is used by grouping tiles during the sorting stage, while allowing rasterization to proceed with the original small tiles by using bitmasks in the rasterization stage. GS-TG is a lossless method requiring no retraining or fine-tuning and it can be seamlessly integrated with previous 3D-GS optimization techniques. Experimental results show that GS-TG achieves an average speed-up of 1.54 times over state-of-the-art 3D-GS accelerators.
Problem
Research questions and friction points this paper is trying to address.
Reducing redundant sorting in 3D Gaussian Splatting rendering
Preserving rasterization efficiency while increasing tile size
Accelerating 3D-GS to meet real-time FPS demands
Innovation
Methods, ideas, or system contributions that make the work stand out.
Tile grouping reduces redundant sorting operations
Bitmask identifies relevant tiles for rasterization efficiency
Lossless method integrates with existing optimization techniques
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