ViM-Disparity: Bridging the Gap of Speed, Accuracy and Memory for Disparity Map Generation
On the map: State-space sequence models (S4, Mamba) → Vision Mamba backbones
Abstract (arXiv)
In this work we propose a Visual Mamba (ViM) based architecture, to dissolve the existing trade-off for real-time and accurate model with low computation overhead for disparity map generation (DMG). Moreover, we proposed a performance measure that can jointly evaluate the inference speed, computation overhead and the accurateness of a DMG model. The code implementation and corresponding models are available at: https://github.com/MBora/ViM-Disparity.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 26 of its 40 references that are in the map. First posted on arXiv 21 Dec 2024.
Builds on
26 of 40 references.
Closest ancestors · 2
Same direction, newest first
- 2024MambaVision: A Hybrid Mamba-Transformer Vision Backbone10 Jul
- 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model17 Jan
Other references · 24
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
- 2018Pyramid Stereo Matching Networkcited by 486
- 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 348
- 2020Virtual KITTI 2cited by 345
- 2022Unifying Flow, Stereo and Depth Estimationcited by 270
- 2023Iterative Geometry Encoding Volume for Stereo Matchingcited by 188
- 2022Attention Concatenation Volume for Accurate and Efficient Stereo Matchingcited by 130
- 2019Hierarchical Deep Stereo Matching on High-resolution Imagescited by 126
- 2020HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matchingcited by 120
12 more
- 2018StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Predictioncited by 88
- 2019Learning monocular depth estimation infusing traditional stereo knowledgecited by 64
- 2020PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matchingcited by 62
- 2018Anytime Stereo Image Depth Estimation on Mobile Devicescited by 54
- 2019Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistencycited by 40
- 2018EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matchingcited by 32
- 2021Digging into Uncertainty in Self-supervised Multi-view Stereocited by 31
- 2019MVS^2: Deep Unsupervised Multi-view Stereo with Multi-View Symmetrycited by 30
- 2021H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometrycited by 12
- 2019Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networkscited by 7
- 2024Distill-then-prune: An Efficient Compression Framework for Real-time Stereo Matching Network on Edge Devicescited by 2
- 2024Recent Advancement in 3D Biometrics using Monocular Cameracited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA
- 2024Mamba-R: Vision Mamba ALSO Needs Registers
- 2024Famba-V: Fast Vision Mamba with Cross-Layer Token Fusion
- 2024QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model
- 2024VMamba: Visual State Space Model
- 2024Visual Mamba: A Survey and New Outlooks
- 2024A Survey on Visual Mamba
- 2024LocalMamba: Visual State Space Model with Windowed Selective Scan