mlsanityData to 7 Oct 2026

Binary TTC: A Temporal Geofence for Autonomous Navigation

arXiv 2101.04777 ↗First posted 12 Jan 2021cs.CV cs.ROcited by 20

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Abstract (arXiv)

Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene -- even for humans. TTC presents several advantages, including requiring only a monocular, uncalibrated camera. However, regressing TTC for each pixel is not straightforward, and most existing methods make over-simplifying assumptions about the scene. We address this challenge by estimating TTC via a series of simpler, binary classifications. We predict with low latency whether the observer will collide with an obstacle within a certain time, which is often more critical than knowing exact, per-pixel TTC. For such scenarios, our method offers a temporal geofence in 6.4 ms -- over 25x faster than existing methods. Our approach can also estimate per-pixel TTC with arbitrarily fine quantization (including continuous values), when the computational budget allows for it. To the best of our knowledge, our method is the first to offer TTC information (binary or coarsely quantized) at sufficiently high frame-rates for practical use.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 29 of its 52 references that are in the map. First posted on arXiv 12 Jan 2021.

Builds on

29 of 52 references.

References · 29

  1. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  2. 2015Fast R-CNNcited by 2,930
  3. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  4. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  5. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  6. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  7. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  8. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  9. 2018Pyramid Stereo Matching Networkcited by 486
  10. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  11. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 359
  12. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 341
17 more
  1. 2018DeepMVS: Learning Multi-view Stereopsiscited by 277
  2. 2019GA-Net: Guided Aggregation Net for End-to-end Stereo Matchingcited by 242
  3. 2020Consistent Video Depth Estimationcited by 220
  4. 2016DeMoN: Depth and Motion Network for Learning Monocular Stereocited by 200
  5. 2020Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Cameracited by 166
  6. 2018DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistencycited by 148
  7. 2019DPSNet: End-to-end Deep Plane Sweep Stereocited by 115
  8. 2018Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understandingcited by 101
  9. 2018StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Predictioncited by 88
  10. 2018Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimationcited by 69
  11. 2019Neural RGB->D Sensing: Depth and Uncertainty from a Video Cameracited by 69
  12. 2020Self-Supervised Monocular Scene Flow Estimationcited by 64
  13. 2019Deep Rigid Instance Scene Flowcited by 54
  14. 2019SENSE: a Shared Encoder Network for Scene-flow Estimationcited by 38
  15. 2020Bi3D: Stereo Depth Estimation via Binary Classificationscited by 34
  16. 2020A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusionscited by 9
  17. 2018Dense Scene Flow from Stereo Disparity and Optical Flowcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 4; 2024: 5; 2025: 2; 2026: 1.

20 papers.

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  1. 2021CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimationcited by 56
  2. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  3. 2025Zero-Shot Monocular Scene Flow Estimation in the Wildcited by 20
  4. 2023Towards Anytime Optical Flow Estimation with Event Camerascited by 15
  5. 2022M-FUSE: Multi-frame Fusion for Scene Flow Estimationcited by 14
  6. 2023RiskBench: A Scenario-based Benchmark for Risk Identificationcited by 12
  7. 2022JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenescited by 11
  8. 2023Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusioncited by 10
  9. 2024Event-Aided Time-to-Collision Estimation for Autonomous Drivingcited by 8
  10. 2021Consistent Semantic Attacks on Optical Flowcited by 4
  11. 2024ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Videocited by 3
  12. 2024A Safety-Adapted Loss for Pedestrian Detection in Automated Drivingcited by 2
8 more
  1. 2025EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimationcited by 2
  2. 2022TTCDist: Fast Distance Estimation From an Active Monocular Camera Using Time-to-Contactcited by 1
  3. 2022Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Imagecited by 1
  4. 2023TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarioscited by 1
  5. 2022GoToNet: Fast Monocular Scene Exposure and Explorationcited by 0
  6. 2024oTTC: Object Time-to-Contact for Motion Estimation in Autonomous Drivingcited by 0
  7. 2024ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Videocited by 0
  8. 2026Time-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environmentscited by 0