mlsanityData to 14 Oct 2026

Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues

arXiv 2305.12955 ↗First posted 22 May 2023cs.CVcited by 7

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Abstract (arXiv)

We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captures, Gated Stereo exploits multi-view cues alongside time-of-flight intensity cues from active gating. To this end, we propose a depth estimation method with a monocular and stereo depth prediction branch which are combined in a final fusion stage. Each block is supervised through a combination of supervised and gated self-supervision losses. To facilitate training and validation, we acquire a long-range synchronized gated stereo dataset for automotive scenarios. We find that the method achieves an improvement of more than 50 % MAE compared to the next best RGB stereo method, and 74 % MAE to existing monocular gated methods for distances up to 160 m. Our code,models and datasets are available here.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 45 of its 72 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

45 of 72 references.

References · 45

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  3. 2021Vision Transformers for Dense Predictioncited by 1,803
  4. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  5. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  6. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  7. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  8. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
  9. 2018Pyramid Stereo Matching Networkcited by 489
  10. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 439
  11. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  12. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 360
33 more
  1. 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 353
  2. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  3. 2017Road Extraction by Deep Residual U-Netcited by 333
  4. 2017Sparsity Invariant CNNscited by 326
  5. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
  6. 2019High-Resolution Representations for Labeling Pixels and Regionscited by 211
  7. 2016DeMoN: Depth and Motion Network for Learning Monocular Stereocited by 201
  8. 2020Non-Local Spatial Propagation Network for Depth Completioncited by 195
  9. 2020Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformerscited by 190
  10. 2017Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagecited by 161
  11. 2021HRFormer: High-Resolution Transformer for Dense Predictioncited by 149
  12. 2021PENet: Towards Precise and Efficient Image Guided Depth Completioncited by 146
  13. 2022Attention Concatenation Volume for Accurate and Efficient Stereo Matchingcited by 130
  14. 2019Hierarchical Deep Stereo Matching on High-resolution Imagescited by 126
  15. 2019Learning the Depths of Moving People by Watching Frozen Peoplecited by 120
  16. 2019Learning Guided Convolutional Network for Depth Completioncited by 111
  17. 2018Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understandingcited by 101
  18. 2017SfM-Net: Learning of Structure and Motion from Videocited by 89
  19. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
  20. 2021Unsupervised Depth Completion with Calibrated Backprojection Layerscited by 61
  21. 2021SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representationscited by 55
  22. 2020Bi3D: Stereo Depth Estimation via Binary Classificationscited by 34
  23. 2019A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?cited by 32
  24. 2020LIBRE: The Multiple 3D LiDAR Datasetcited by 26
  25. 2019Gated2Depth: Real-time Dense Lidar from Gated Imagescited by 23
  26. 2019Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Drivingcited by 21
  27. 2019Self-supervised Object Motion and Depth Estimation from Videocited by 17
  28. 2021Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimationcited by 15
  29. 2019Pixel-Accurate Depth Evaluation in Realistic Driving Scenarioscited by 12
  30. 2015Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedocited by 8
  31. 2021Gated2Gated: Self-Supervised Depth Estimation from Gated Imagescited by 6
  32. 2019Sparse2Dense: From direct sparse odometry to dense 3D reconstructioncited by 4
  33. 2019Learning Super-resolved Depth from Active Gated Imagingcited by 3

Built on by

Cited by year

Citing papers per year: 2024: 4; 2025: 3.

7 papers.

  1. 2024A Survey on Deep Stereo Matching in the Twentiescited by 23
  2. 2025SAMFusion: Sensor-Adaptive Multimodal Fusion for 3D Object Detection in Adverse Weathercited by 12
  3. 2025Diving into the Fusion of Monocular Priors for Generalized Stereo Matchingcited by 10
  4. 2024Cross-spectral Gated-RGB Stereo Depth Estimationcited by 8
  5. 2025Self-Supervised Sparse Sensor Fusion for Long Range Perceptioncited by 8
  6. 2024Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Visioncited by 7
  7. 2024Gated Fields: Learning Scene Reconstruction from Gated Videoscited by 3