Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching

TL;DR

This paper introduces a memory- and time-efficient cascade cost volume method for high-resolution multi-view stereo and stereo matching, significantly improving accuracy while reducing resource consumption.

Contribution

The proposed cascade cost volume approach enhances existing methods by enabling high-resolution outputs with lower memory and computational costs through a multi-scale, adaptive strategy.

Findings

23.1% improvement on DTU benchmark

50.6% reduction in GPU memory usage

74.2% faster runtime

Abstract

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity. These methods are limited when high-resolution outputs are needed since the memory and time costs grow cubically as the volume resolution increases. In this paper, we propose a both memory and time efficient cost volume formulation that is complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes. First, the proposed cost volume is built upon a standard feature pyramid encoding geometry and context at gradually finer scales. Then, we can narrow the depth (or disparity) range of each stage by the depth (or disparity) map from the previous stage. With gradually higher cost volume resolution and adaptive adjustment of depth (or disparity) intervals, the output is recovered in a coarser to fine manner. We apply the cascade cost volume to the representative MVS-Net, and obtain a 23.1% improvement on DTU benchmark (1st place), with 50.6% and 74.2% reduction in GPU memory and run-time. It is also the state-of-the-art learning-based method on Tanks and Temples benchmark. The statistics of accuracy, run-time and GPU memory on other representative stereo CNNs also validate the effectiveness of our proposed method.