EAGLES：具有轻量级编码的高效加速3D高斯
EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
AI总结：
本文提出EAGLES方法，通过量化嵌入和从粗到细的训练策略及剪枝阶段，显著降低3D高斯点云的内存存储需求并加速训练与渲染，在保持重建质量的同时实现10-20倍的内存减少。
摘要：
最近，3D Gaussian splatting (3D-GS) 在新视角场景合成中变得流行。它解决了与Neural Radiance Fields (NeRFs) 相关的训练时间长和渲染速度慢的挑战。通过3D高斯的快速可微光栅化，3D-GS实现了实时渲染和加速训练。然而，它们在训练和存储方面需要大量内存资源，因为每个场景的点云表示需要数百万个高斯。我们提出了一种利用量化嵌入来显著减少每点内存存储需求的技术，以及一种从粗到细的训练策略，用于更快更稳定地优化高斯点云。我们的方法开发了一个剪枝阶段，产生使用更少高斯的场景表示，从而加快训练时间和渲染速度，实现高分辨率场景的实时渲染。我们将存储内存减少了一个数量级以上，同时保持重建质量。我们在多种数据集和场景上验证了方法的有效性，在保持视觉质量的同时，消耗的内存减少10-20倍，训练/推理速度更快。项目页面和代码可在 https://efficientgaussian.github.io 获取
英文摘要：
Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associated with Neural Radiance Fields (NeRFs). Through rapid, differentiable rasterization of 3D Gaussians, 3D-GS achieves real-time rendering and accelerated training. They, however, demand substantial memory resources for both training and storage, as they require millions of Gaussians in their point cloud representation for each scene. We present a technique utilizing quantized embeddings to significantly reduce per-point memory storage requirements and a coarse-to-fine training strategy for a faster and more stable optimization of the Gaussian point clouds. Our approach develops a pruning stage which results in scene representations with fewer Gaussians, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes. We reduce storage memory by more than an order of magnitude all while preserving the reconstruction quality. We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20x lesser memory and faster training/inference speed. Project page and code is available https://efficientgaussian.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
