WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching
- URL: http://arxiv.org/abs/2603.24836v2
- Date: Mon, 30 Mar 2026 06:19:26 GMT
- Title: WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching
- Abstract summary: WAFT-Stereo is a warping-based method for stereo matching.<n>It ranks first on ETH3D (BP-0.5), Middlebury (RMSE), and KITTI (all metrics)<n>It reduces the zero-shot error by 81% on ETH3D, while being 1.8-6.7x faster than competitive methods.
- Score: 36.58978128337916
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce WAFT-Stereo, a simple and effective warping-based method for stereo matching. WAFT-Stereo demonstrates that cost volumes, a common design used in many leading methods, are not necessary for strong performance and can be replaced by warping with improved efficiency. WAFT-Stereo ranks first on ETH3D (BP-0.5), Middlebury (RMSE), and KITTI (all metrics), reducing the zero-shot error by 81% on ETH3D, while being 1.8-6.7x faster than competitive methods. Code and model weights are available at https://github.com/princeton-vl/WAFT-Stereo.
Related papers
- LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization [21.209111801863546]
We present textitLiteMatch, a lightweight stereo matching framework that achieves strong zero-shot generalization without expensive 3D convolutions.<n>LiteMatch employs two complementary encoders: a Cross-View Correspondence (CVCE) to capture global cross-view interactions, and a High-Frequency Driving (HFE) that enhances fine structural details via FFT-based frequency cues.<n>With a flexible design ranging from 3.36M to 9.58M parameters, LiteMatch achieves exceptional zero-shot generalization, delivering competitive EPE and D1 performance across Scene Flow, KITTI, Middlebury, ETH3D
arXiv Detail & Related papers (2026-06-30T13:20:08Z) - Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching [96.63979376005277]
Lite Any Stereo V2 is an ultra-fast model series designed for efficient zero-shot stereo matching.<n>LAS2 is developed from both architecture and training perspectives.<n>LAS2-H achieves stronger overall zero-shot performance than the iterative method Fast-FoundationStereo.
arXiv Detail & Related papers (2026-06-23T11:45:09Z) - Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency [61.68376148916503]
We introduce Full-Duplex-Bench-v3 (FDB-v3), a benchmark for evaluating spoken language models under naturalistic speech conditions and multi-step tool use.<n>Unlike prior work, our dataset consists entirely of real human audio annotated for five disfluency categories, paired with scenarios requiring chained API calls across four task domains.<n>We evaluate six model configurations -- GPT-Realtime, Gemini Live 2.5, Gemini Live 3.1, Grok, Ultravox v0.7, and a traditional Cascaded pipeline (Whisper$rightarrow$rightarrow$TTS) -- across accuracy, latency, and turn-
arXiv Detail & Related papers (2026-04-06T16:46:52Z) - WAFT: Warping-Alone Field Transforms for Optical Flow [36.58978128337916]
We introduce Warping-Alone Field Transforms (WAFT), a simple and effective method for optical flow.<n>WAFT is similar to RAFT but replaces cost volume with high-resolution warping, achieving better accuracy with lower memory cost.
arXiv Detail & Related papers (2025-06-26T17:47:59Z) - LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation [27.00836175513738]
LightStereo is a cutting-edge stereo-matching network crafted to accelerate the matching process.<n>Our breakthrough lies in enhancing performance through a dedicated focus on the channel dimension of the 3D cost volume.<n>LightStereo achieves a competitive EPE metric in the SceneFlow datasets while demanding a minimum of only 22 GFLOPs and 17 ms of runtime.
arXiv Detail & Related papers (2024-06-28T11:11:24Z) - 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) - Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation [73.31524865643709]
We present a plug-and-play pruning-and-recovering framework, called Hourglass Tokenizer (HoT), for efficient transformer-based 3D pose estimation from videos.
Our HoDT begins with pruning pose tokens of redundant frames and ends with recovering full-length tokens, resulting in a few pose tokens in the intermediate transformer blocks.
Our method can achieve both high efficiency and estimation accuracy compared to the original VPT models.
arXiv Detail & Related papers (2023-11-20T18:59:51Z) - 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) - RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching [60.44903340167672]
We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT.
We introduce multi-level convolutional GRUs, which more efficiently propagate information across the image.
A modified version of RAFT-Stereo can perform accurate real-time inference.
arXiv Detail & Related papers (2021-09-15T19:27:31Z) - VATT: Transformers for Multimodal Self-Supervised Learning from Raw
Video, Audio and Text [60.97904439526213]
Video-Audio-Text Transformer (VATT) takes raw signals as inputs and extracts multimodal representations that are rich enough to benefit a variety of downstream tasks.
We train VATT end-to-end from scratch using multimodal contrastive losses and evaluate its performance by the downstream tasks of video action recognition, audio event classification, image classification, and text-to-video retrieval.
arXiv Detail & Related papers (2021-04-22T17:07:41Z) - Towards Fast, Accurate and Stable 3D Dense Face Alignment [73.01620081047336]
We propose a novel regression framework named 3DDFA-V2 which makes a balance among speed, accuracy and stability.
We present a virtual synthesis method to transform one still image to a short-video which incorporates in-plane and out-of-plane face moving.
arXiv Detail & Related papers (2020-09-21T15:37:37Z) - 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.