Image-Coupled Volume Propagation for Stereo Matching

TL;DR

This paper introduces a novel stereo matching framework that combines image volume processing with feature matching, reducing computational complexity while maintaining high accuracy, and achieving competitive benchmark results.

Contribution

It proposes a new integrated approach merging image volume and feature matching concepts, enabling faster stereo matching with preserved accuracy.

Findings

Achieves second place on KITTI2012 and ETH3D benchmarks.

Reduces 3D convolution scale without losing accuracy.

Improves fine-scale detail resolution.

Abstract

Several leading methods on public benchmarks for depth-from-stereo rely on memory-demanding 4D cost volumes and computationally intensive 3D convolutions for feature matching. We suggest a new way to process the 4D cost volume where we merge two different concepts in one deeply integrated framework to achieve a symbiotic relationship. A feature matching part is responsible for identifying matching pixels pairs along the baseline while a concurrent image volume part is inspired by depth-from-mono CNNs. However, instead of predicting depth directly from image features, it provides additional context to resolve ambiguities during pixel matching. More technically, the processing of the 4D cost volume is separated into a 2D propagation and a 3D propagation part. Starting from feature maps of the left image, the 2D propagation assists the 3D propagation part of the cost volume at different layers by adding visual features to the geometric context. By combining both parts, we can safely reduce the scale of 3D convolution layers in the matching part without sacrificing accuracy. Experiments demonstrate that our end-to-end trained CNN is ranked 2nd on KITTI2012 and ETH3D benchmarks while being significantly faster than the 1st-ranked method. Furthermore, we notice that the coupling of image and matching-volume improves fine-scale details as demonstrated by our qualitative analysis.