mlsanityData to 14 Oct 2026

LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation

arXiv 2406.19833 ↗First posted 28 Jun 2024cs.CVcited by 28

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Abstract (arXiv)

We present LightStereo, a cutting-edge stereo-matching network crafted to accelerate the matching process. Departing from conventional methodologies that rely on aggregating computationally intensive 4D costs, LightStereo adopts the 3D cost volume as a lightweight alternative. While similar approaches have been explored previously, our breakthrough lies in enhancing performance through a dedicated focus on the channel dimension of the 3D cost volume, where the distribution of matching costs is encapsulated. Our exhaustive exploration has yielded plenty of strategies to amplify the capacity of the pivotal dimension, ensuring both precision and efficiency. We compare the proposed LightStereo with existing state-of-the-art methods across various benchmarks, which demonstrate its superior performance in speed, accuracy, and resource utilization. LightStereo achieves a competitive EPE metric in the SceneFlow datasets while demanding a minimum of only 22 GFLOPs and 17 ms of runtime, and ranks 1st on KITTI 2015 among real-time models. Our comprehensive analysis reveals the effect of 2D cost aggregation for stereo matching, paving the way for real-world applications of efficient stereo systems. Code will be available at https://github.com/XiandaGuo/OpenStereo.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 47 of its 66 references that are in the map. First posted on arXiv 28 Jun 2024.

Builds on

47 of 66 references.

References · 47

  1. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  2. 2017Squeeze-and-Excitation Networkscited by 4,789
  3. 2019Searching for MobileNetV3cited by 1,994
  4. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  5. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  6. 2018Pyramid Stereo Matching Networkcited by 489
  7. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 417
  8. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 360
  9. 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 353
  10. 2022SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentationcited by 252
  11. 2019GA-Net: Guided Aggregation Net for End-to-end Stereo Matchingcited by 244
  12. 2019Group-wise Correlation Stereo Networkcited by 235
35 more
  1. 2017Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Networkcited by 208
  2. 2020AANet: Adaptive Aggregation Network for Efficient Stereo Matchingcited by 202
  3. 2022Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlationcited by 197
  4. 2023Iterative Geometry Encoding Volume for Stereo Matchingcited by 190
  5. 2023EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attentioncited by 172
  6. 2020Hierarchical Neural Architecture Search for Deep Stereo Matchingcited by 166
  7. 2021CFNet: Cascade and Fused Cost Volume for Robust Stereo Matchingcited by 154
  8. 2018Learning Depth with Convolutional Spatial Propagation Networkcited by 136
  9. 2022Attention Concatenation Volume for Accurate and Efficient Stereo Matchingcited by 130
  10. 2023RepViT: Revisiting Mobile CNN From ViT Perspectivecited by 123
  11. 2020HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matchingcited by 120
  12. 2019DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatchcited by 114
  13. 2020Strip Pooling: Rethinking Spatial Pooling for Scene Parsingcited by 114
  14. 2019Domain-invariant Stereo Matching Networkscited by 109
  15. 2024Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matchingcited by 99
  16. 2018StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Predictioncited by 88
  17. 2018SegStereo: Exploiting Semantic Information for Disparity Estimationcited by 88
  18. 2021MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matchingcited by 82
  19. 2018Real-time self-adaptive deep stereocited by 80
  20. 2022Accurate and Efficient Stereo Matching via Attention Concatenation Volumecited by 59
  21. 2024Rewrite the Starscited by 55
  22. 2018Anytime Stereo Image Depth Estimation on Mobile Devicescited by 54
  23. 2021Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitationcited by 47
  24. 2021Bilateral Grid Learning for Stereo Matching Networkscited by 46
  25. 2019EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detectioncited by 39
  26. 2021Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networkscited by 32
  27. 2020FADNet: A Fast and Accurate Network for Disparity Estimationcited by 31
  28. 2020AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matchingcited by 31
  29. 2023OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baselinecited by 30
  30. 2021PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matchingcited by 28
  31. 2021A Decomposition Model for Stereo Matchingcited by 25
  32. 2023Adaptive Multi-Modal Cross-Entropy Loss for Stereo Matchingcited by 19
  33. 2023CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interactioncited by 17
  34. 2024Federated Online Adaptation for Deep Stereocited by 12
  35. 2021FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networkscited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 19; 2026: 7.

28 papers.

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  1. 2024SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Modelscited by 29
  2. 2025Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matchingcited by 28
  3. 2025BANet: Bilateral Aggregation Network for Mobile Stereo Matchingcited by 12
  4. 2025Light of Normals: Unified Feature Representation for Universal Photometric Stereocited by 9
  5. 2025Stereo Any Video: Temporally Consistent Stereo Matchingcited by 8
  6. 2024Stereo Anything: Unifying Zero-shot Stereo Matching with Large-Scale Mixed Datacited by 5
  7. 2025Lite Any Stereo: Efficient Zero-Shot Stereo Matchingcited by 4
  8. 2025FastRSR: Efficient and Accurate Road Surface Reconstruction from Bird's Eye Viewcited by 3
  9. 2025StereoCarla: A High-Fidelity Driving Dataset for Generalizable Stereocited by 3
  10. 2025FastViDAR: Real-Time Omnidirectional Depth Estimation via Alternative Hierarchical Attentioncited by 2
  11. 2025StereoDETR: Stereo-based Transformer for 3D Object Detectioncited by 2
  12. 2025MAFNet:Multi-frequency Adaptive Fusion Network for Real-time Stereo Matchingcited by 2
16 more
  1. 2025ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based Refinementcited by 1
  2. 2025ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Drivingcited by 1
  3. 2025CogStereo: Neural Stereo Matching with Implicit Spatial Cognition Embeddingcited by 1
  4. 2025RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereocited by 1
  5. 2026Humanoid-OmniOcc: Stereo-Based Full-View Occupancy Dataset for Embodied AIcited by 1
  6. 2025DispBench: Benchmarking Disparity Estimation to Synthetic Corruptionscited by 0
  7. 2025Pixel-Optimization-Free Patch Attack on Stereo Depth Estimationcited by 0
  8. 2025ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo Matchingcited by 0
  9. 2025MatchAttention: Embedding Explicit Matching Constraints into Attention for Efficient Stereo Matchingcited by 0
  10. 2025PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matchingcited by 0
  11. 2026Pip-Stereo: Progressive Iterations Pruner for Iterative Optimization based Stereo Matchingcited by 0
  12. 2026Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matchingcited by 0
  13. 2026LinStereo: Linear-Complexity Global Attention for Multi-Scale Iterative Stereo Matchingcited by 0
  14. 2026URS-Stereo: Uncertainty-Guided Residual Search for Real-Time Stereo Matchingcited by 0
  15. 2026TrustVLA: Mechanism-Guided Inference-Time Defense Against Vision-Language-Action Backdoorscited by 0
  16. 2026STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matchingcited by 0