ZipMVS: Multi-View Stereo with Compressed Cost Volumes
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 ...
| Authors | Guanglin Jin, Hongshan Yu, Javier Civera et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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