SafeguardGS: 3D Gaussian Primitive Pruning While Avoiding Catastrophic Scene Destruction

• URL: http://arxiv.org/abs/2405.17793v1
• Date: Tue, 28 May 2024 03:41:36 GMT
• Title: SafeguardGS: 3D Gaussian Primitive Pruning While Avoiding Catastrophic Scene Destruction
• Authors: Yongjae Lee, Zhaoliang Zhang, Deliang Fan,
• Abstract summary: 3DGS has made a significant stride in novel view synthesis, demonstrating top-notch rendering quality while achieving real-time rendering speed. The excessively large number of Gaussian primitives resulting from 3DGS' suboptimal densification process poses a major challenge, slowing down frame-per-second (FPS) and demanding considerable memory cost. We first categorize 3DGS pruning techniques into two types: Cross-view pruning and pixel-wise pruning, which differ in their approaches to rank primitives.
• Score: 45.654397516679495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has made a significant stride in novel view synthesis, demonstrating top-notch rendering quality while achieving real-time rendering speed. However, the excessively large number of Gaussian primitives resulting from 3DGS' suboptimal densification process poses a major challenge, slowing down frame-per-second (FPS) and demanding considerable memory cost, making it unfavorable for low-end devices. To cope with this issue, many follow-up studies have suggested various pruning techniques, often in combination with different score functions, to optimize rendering performance. Nonetheless, a comprehensive discussion regarding their effectiveness and implications across all techniques is missing. In this paper, we first categorize 3DGS pruning techniques into two types: Cross-view pruning and pixel-wise pruning, which differ in their approaches to rank primitives. Our subsequent experiments reveal that while cross-view pruning leads to disastrous quality drops under extreme Gaussian primitives decimation, the pixel-wise pruning technique not only sustains relatively high rendering quality with minuscule performance degradation but also provides a reasonable minimum boundary for pruning. Building on this observation, we further propose multiple variations of score functions and empirically discover that the color-weighted score function outperforms others for discriminating insignificant primitives for rendering. We believe our research provides valuable insights for optimizing 3DGS pruning strategies for future works.

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