Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality

TL;DR

This paper introduces a real-time, video-based stereo matching framework optimized for XR devices, utilizing a novel synthetic indoor dataset and temporal feature reuse to achieve high accuracy and 134fps inference.

Contribution

The paper presents a new synthetic indoor stereo dataset and a temporal cost aggregation method enabling real-time stereo matching for XR with high accuracy and efficiency.

Findings

Achieves 134fps stereo matching on desktop and 30fps on HMD.

Outperforms current state-of-the-art methods in accuracy and speed.

Provides a valuable dataset for indoor AR/VR stereo vision research.

Abstract

Real-time Stereo Matching is a cornerstone algorithm for many Extended Reality (XR) applications, such as indoor 3D understanding, video pass-through, and mixed-reality games. Despite significant advancements in deep stereo methods, achieving real-time depth inference with high accuracy on a low-power device remains a major challenge. One of the major difficulties is the lack of high-quality indoor video stereo training datasets captured by head-mounted VR/AR glasses. To address this issue, we introduce a novel video stereo synthetic dataset that comprises photorealistic renderings of various indoor scenes and realistic camera motion captured by a 6-DoF moving VR/AR head-mounted display (HMD). This facilitates the evaluation of existing approaches and promotes further research on indoor augmented reality scenarios. Our newly proposed dataset enables us to develop a novel framework for continuous video-rate stereo matching. As another contribution, our dataset enables us to proposed a new video-based stereo matching approach tailored for XR applications, which achieves real-time inference at an impressive 134fps on a standard desktop computer, or 30fps on a battery-powered HMD. Our key insight is that disparity and contextual information are highly correlated and redundant between consecutive stereo frames. By unrolling an iterative cost aggregation in time (i.e. in the temporal dimension), we are able to distribute and reuse the aggregated features over time. This approach leads to a substantial reduction in computation without sacrificing accuracy. We conducted extensive evaluations and comparisons and demonstrated that our method achieves superior performance compared to the current state-of-the-art, making it a strong contender for real-time stereo matching in VR/AR applications.