WAVE-Stereo: Warp-Aligned Volume Encoding for Stereo Matching

• URL: http://arxiv.org/abs/2607.13674v1
• Date: Wed, 15 Jul 2026 10:15:05 GMT
• Title: WAVE-Stereo: Warp-Aligned Volume Encoding for Stereo Matching
• Abstract summary: WAVE-Stereo is built on a core insight: correlation volumes and feature warping provide complementary matching cues.<n>On five real-world benchmarks, WAVE-Stereo achieves 3.18% D1-all on KITTI 2015, 4.42% Bad-2.0 on Booster, and 66ms real-time inference, striking a favorable balance between accuracy and efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing iterative stereo matching methods primarily adopt two types of correspondence representation: explicit matching search via correlation volumes and local residual refinement via warped features, yet the two remain separately modeled. We propose WAVE-Stereo, built on a core insight: correlation volumes and feature warping provide complementary matching cues. \textbf{GeoWarp Correspondence Encoder (GWCE)} encodes matching search, residual alignment, and disparity prior in parallel at the ConvGRU input. To mitigate matching degradation in textureless regions, we propose \textbf{Periodic Global Context Propagation (PGCP)}, which propagates global spatial information in a periodic manner. On five real-world benchmarks -- Middlebury, ETH3D, KITTI 2012, KITTI 2015, and Booster -- WAVE-Stereo achieves competitive zero-shot generalization accuracy without any external foundation model prior, achieving 3.18\% D1-all on KITTI 2015, 4.42\% Bad-2.0 on Booster, and 66ms real-time inference, striking a favorable balance between accuracy and efficiency. Our code is available at https://github.com/yamanoko-do/WAVE-Stereo.

Related papers

• Weaving Light and Time: Unified Harmonic-Geometric Representation Learning for Dense RGB-Event Parsing [55.13802890769086]
  We present Evita, the first unified backbone specifically engineered for dedicated dense RGB-Event parsing.<n>To achieve profound modal synergy, Evita embeds a suite of intrinsic co-learning modules directly into every encoder layer.<n>Evita establishes new state-of-the-art metrics while delivering a superior accuracy-latency trade-off for real-time multimodal perception.
  arXiv  Detail & Related papers  (2026-07-10T06:52:09Z)
• Chiaroscuro Attention: Spending Compute in the Dark [0.0]
  CHIAR-Former is a 4-layer hybrid transformer that routes each token to one of three operators.<n>A purpose-designed DCT+Attention-only variant achieves Val PPL 36.54 on WikiText-103.
  arXiv  Detail & Related papers  (2026-06-06T20:38:42Z)
• PrismAudio: Decomposed Chain-of-Thoughts and Multi-dimensional Rewards for Video-to-Audio Generation [57.864929968616586]
  Video-to-Audio (V2A) generation requires balancing four critical perceptual dimensions.<n>We introduce PrismAudio, the first framework to integrate Reinforcement Learning into V2A generation with specialized Chain-of-Thought (CoT) planning.
  arXiv  Detail & Related papers  (2025-11-24T07:11:12Z)
• Decoupling Bidirectional Geometric Representations of 4D cost volume with 2D convolution [40.103929972279126]
  We present a deployment-friendly 4D cost aggregation network DBStereo.<n>It is based on pure 2D convolutions and achieves real-time performance and impressive accuracy simultaneously.
  arXiv  Detail & Related papers  (2025-09-02T15:21:49Z)
• RomniStereo: Recurrent Omnidirectional Stereo Matching [6.153793254880079]
  We propose a recurrent omnidirectional stereo matching (RomniStereo) algorithm. Our best model improves the average MAE metric by 40.7% over the previous SOTA baseline. When visualizing the results, our models demonstrate clear advantages on both synthetic and realistic examples.
  arXiv  Detail & Related papers  (2024-01-09T04:06:01Z)
• Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching [77.133400999703]
  Correlation based stereo matching has achieved outstanding performance. Current methods with a fixed model do not work uniformly well across various datasets. This paper proposes a new perspective to dynamically calculate correlation for robust stereo matching.
  arXiv  Detail & Related papers  (2023-07-26T09:47:37Z)
• RoMa: Robust Dense Feature Matching [17.015362716393216]
  Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene. We propose a model, leveraging frozen pretrained features from the foundation model DINOv2. To further improve robustness, we propose a tailored transformer match decoder.
  arXiv  Detail & Related papers  (2023-05-24T17:59:04Z)
• AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach [50.855679274530615]
  We present a novel domain-adaptive approach called AdaStereo to align multi-level representations for deep stereo matching networks. Our models achieve state-of-the-art cross-domain performance on multiple benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo. Our method is robust to various domain adaptation settings, and can be easily integrated into quick adaptation application scenarios and real-world deployments.
  arXiv  Detail & Related papers  (2021-12-09T15:10:47Z)
• Deep Hough Voting for Robust Global Registration [52.40611370293272]
  We present an efficient framework for pairwise registration of real-world 3D scans, leveraging Hough voting in the 6D transformation parameter space. Our method outperforms state-of-the-art methods on 3DMatch and 3DLoMatch benchmarks while achieving comparable performance on KITTI odometry dataset.
  arXiv  Detail & Related papers  (2021-09-09T14:38:06Z)
• Do End-to-end Stereo Algorithms Under-utilize Information? [7.538482310185133]
  We show how deep adaptive filtering and differentiable semi-global aggregation can be integrated in 2D and 3D convolutional networks for end-to-end stereo matching. The improvements are due to utilizing RGB information from the images as a signal to dynamically guide the matching process.
  arXiv  Detail & Related papers  (2020-10-14T18:32:39Z)
• AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching [50.06646151004375]
  A novel domain-adaptive pipeline called AdaStereo aims to align multi-level representations for deep stereo matching networks. Our AdaStereo models achieve state-of-the-art cross-domain performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D, and DrivingStereo.
  arXiv  Detail & Related papers  (2020-04-09T16:15:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.