SafeguardGS: 3D Gaussian Primitive Pruning While Avoiding Catastrophic Scene Destruction
<|reference_start|>SafeguardGS: 3D Gaussian Primitive Pruning While Avoiding Catastrophic Scene Destruction: 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.<|reference_end|>
@article{lee2024safeguardgs:,
    title={SafeguardGS: 3D Gaussian Primitive Pruning While Avoiding Catastrophic
  Scene Destruction},
    author={Yongjae Lee, Zhaoliang Zhang, Deliang Fan},
    journal={arXiv preprint arXiv:2405.17793},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17793},
    primaryClass={cs.CV}
}