IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching (2409.00638v3)

Abstract: Stereo matching is a core component in many computer vision and robotics systems. Despite significant advances over the last decade, handling matching ambiguities in ill-posed regions and large disparities remains an open challenge. In this paper, we propose a new deep network architecture, called IGEV++, for stereo matching. The proposed IGEV++ constructs Multi-range Geometry Encoding Volumes (MGEV), which encode coarse-grained geometry information for ill-posed regions and large disparities, while preserving fine-grained geometry information for details and small disparities. To construct MGEV, we introduce an adaptive patch matching module that efficiently and effectively computes matching costs for large disparity ranges and/or ill-posed regions. We further propose a selective geometry feature fusion module to adaptively fuse multi-range and multi-granularity geometry features in MGEV. Then, we input the fused geometry features into ConvGRUs to iteratively update the disparity map. MGEV allows to efficiently handle large disparities and ill-posed regions, such as occlusions and textureless regions, and enjoys rapid convergence during iterations. Our IGEV++ achieves the best performance on the Scene Flow test set across all disparity ranges, up to 768px. Our IGEV++ also achieves state-of-the-art accuracy on the Middlebury, ETH3D, KITTI 2012, and 2015 benchmarks. Specifically, IGEV++ achieves a 3.23\% 2-pixel outlier rate (Bad 2.0) on the large disparity benchmark, Middlebury, representing error reductions of 31.9\% and 54.8\% compared to RAFT-Stereo and GMStereo, respectively. We also present a real-time version of IGEV++ that achieves the best performance among all published real-time methods on the KITTI benchmarks. The code is publicly available at https://github.com/gangweix/IGEV and https://github.com/gangweix/IGEV-plusplus.

• The paper introduces IGEV++, a new deep network that uses Multi-range Geometry Encoding Volumes (MGEV) to improve stereo matching accuracy and efficiently handle large disparities and ambiguous regions.
• IGEV++ demonstrates superior performance on datasets like Scene Flow and Middlebury, achieving significant error reductions (31.9% vs RAFT-Stereo) and faster convergence rates than state-of-the-art methods.
• The methodology's combination of high accuracy across disparity ranges and computational efficiency holds promise for real-time applications such as autonomous driving and robotics.

Analysis of IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching

Stereo matching, pivotal in computer vision and robotics, assists in inferring 3D scene geometry from image captures. Despite noteworthy strides in deep learning methodologies, challenges persist in accurately addressing matching ambiguities and large disparities across textures and occluded regions. The paper introduces a novel deep network architecture termed IGEV++, designed to enhance stereo matching by constructing Multi-range Geometry Encoding Volumes (MGEV). This network adeptly addresses spatial ambiguity and efficiently manages large disparities through adaptive patch matching and feature fusion methodologies.

Overview of IGEV++ Architecture

IGEV++ capitalizes on both filtering-based and iterative optimization-based stereo matching methods to forge an efficient architecture capable of handling a wide array of disparity challenges. It constructs MGEVs which encode multi-range geometry information with granular adaptability, serving both large and fine disparity requirements. This encoding ensures greater accuracy in existing ill-posed regions and accelerates convergence during iterative disparity updates.

Key Methodological Components

1. Multi-range Geometry Encoding Volumes (MGEV): MGEVs are pivotal in encoding multiple disparity ranges efficiently. They allow the network to maintain coarse-grained geometry for large disparities while ensuring precise details for smaller disparities. This is achieved through adaptive matching and selective feature fusion.
2. Adaptive Patch Matching: This module computes matching costs efficiently for varying disparity ranges, reinforcing the network’s capacity to handle large disparities and ill-posed regions more effectively than conventional methods.
3. Selective Geometry Feature Fusion: Adopted to amalgamate multi-range and granularity geometry features, this module facilitates enhanced feature indexing, allowing ConvGRUs to iteratively refine disparity maps with increased precision.

Quantitative Insights

The numerical results underscore IGEV++'s superior performance, particularly on the Scene Flow dataset, where it surpasses prevalent methods by significant margins across all disparity ranges (up to 768px). Notably, on the Middlebury dataset, IGEV++ exhibits error reductions of 31.9% and 54.8% compared to RAFT-Stereo and GMStereo, respectively. It also demonstrates the fastest convergence rates, achieving optimal performance with fewer iterations, highlighting its computational efficiency. This is a key development for application scenarios reliant on real-time or near-real-time computational capacity, particularly when high disparity ranges are prevalent.

Comparative Evaluation

IGEV++ shows marked improvements in accuracy and computational efficiency when compared against state-of-the-art methods such as RAFT-Stereo, GMStereo, and PCWNet, as evidenced by evaluations on KITTI and ETH3D benchmarks. The methodology’s ability to balance computational resource demand with high accuracy offers viable real-time application prospects, extending its utility in fields necessitating rapid computational turnarounds, like autonomous driving and robotics.

Implications and Future Prospects

The introduction of IGEV++ has significant implications for the deep stereo matching domain. The robustness across varied disparity ranges and its handling of ill-posed regions signify potential shifts in stereo matching strategies, potentially influencing future research directions toward more adaptive, feature-rich architectures.

Future investigations could explore the extension of these methodologies in volumetric scene representations and multi-camera systems, thereby broadening the application scope in three-dimensional scene reconstruction. Moreover, further refinement of the adaptive patch matching technique could drive increased precision, especially in highly dynamic or texture-varied scenes.

Through IGEV++, the domain finds a potent contender capable of resolving longstanding challenges associated with disparity-variant stereo matching, projecting a trajectory that promises refined accuracy coupled with enhanced computational efficiency.