压缩成本体积，让多视角立体重建更省显存
ZipMVS: Multi-View Stereo with Compressed Cost Volumes
- 用新深度假设策略大幅压缩成本体积
- 在DTU和Tanks and Temples上保持高精度重建
- 适合资源受限场景的实时三维重建
多视角立体（MVS）方法通常能从多张配准的RGB图像中生成高精度3D重建，得益于它们之间丰富的几何约束。然而，其巨大的内存需求仍是航空航天和自主系统等领域部署的主要障碍，这些领域对资源效率要求极高。本文提出ZipMVS，一种专为高效高质量重建设计的MVS方法。我们引入一种新颖的深度假设策略，实现成本体积的显著压缩，从而大幅降低GPU内存占用，同时保持重建精度。在DTU和Tanks and Temples数据集上的实验表明，相比其他注重效率的MVS方法，ZipMVS在重建质量与GPU内存使用之间实现了良好平衡，表现具有竞争力。代码已开源。
原文摘要 · Abstract (English)
Multi-view stereo (MVS) methods typically deliver highly accurate 3D reconstructions from multiple registered RGB images, thanks to the highly informative, geometric constraints between them. However, their substantial memory requirements remain a major obstacle for deployment in domains such as aerospace and autonomous systems, where resource efficiency is critical. In this work, we introduce ZipMVS, an MVS method specifically designed for efficient high-quality reconstruction. We propose a novel depth-hypothesis strategy that enables substantial compression of the cost volume, hence greatly reducing GPU memory consumption while preserving reconstruction accuracy. Experiments on the DTU and Tanks and Temples datasets show that ZipMVS achieves competitive reconstruction quality compared with other efficiency-oriented MVS methods, while achieving a competitive balance between reconstruction quality and GPU memory usage. The code is available at https://github.com/JihnGlyn/ZipMVS
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