mlsanityData to 7 Oct 2026

LEAD: LiDAR Extender for Autonomous Driving

arXiv 2102.07989 ↗First posted 16 Feb 2021cs.CV eess.IVcited by 0

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Abstract (arXiv)

3D perception using sensors under vehicle industrial standard is the rigid demand in autonomous driving. MEMS LiDAR emerges with irresistible trend due to its lower cost, more robust, and meeting the mass-production standards. However, it suffers small field of view (FoV), slowing down the step of its population. In this paper, we propose LEAD, i.e., LiDAR Extender for Autonomous Driving, to extend the MEMS LiDAR by coupled image w.r.t both FoV and range. We propose a multi-stage propagation strategy based on depth distributions and uncertainty map, which shows effective propagation ability. Moreover, our depth outpainting/propagation network follows a teacher-student training fashion, which transfers depth estimation ability to depth completion network without any scale error passed. To validate the LiDAR extension quality, we utilize a high-precise laser scanner to generate a ground-truth dataset. Quantitative and qualitative evaluations show that our scheme outperforms SOTAs with a large margin. We believe the proposed LEAD along with the dataset would benefit the community w.r.t depth researches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 16 Feb 2021.

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25 of 34 references.

References · 25

  1. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  2. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  3. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
  4. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  5. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  6. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  7. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 341
  8. 2017Sparsity Invariant CNNscited by 325
  9. 2020Non-Local Spatial Propagation Network for Depth Completioncited by 194
  10. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
  11. 2018Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Cameracited by 140
  12. 2018Learning Depth with Convolutional Spatial Propagation Networkcited by 135
13 more
  1. 2018DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Imagecited by 129
  2. 2020Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volumecited by 116
  3. 2018Deep Depth Completion of a Single RGB-D Imagecited by 110
  4. 2019CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completioncited by 110
  5. 2018Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understandingcited by 101
  6. 2017Learning Affinity via Spatial Propagation Networkscited by 79
  7. 2020The Edge of Depth: Explicit Constraints between Segmentation and Depthcited by 72
  8. 2018Learning Monocular Depth by Distilling Cross-domain Stereo Networkscited by 64
  9. 2020Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to Endcited by 55
  10. 2018What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?cited by 44
  11. 2019Generating and Exploiting Probabilistic Monocular Depth Estimatescited by 18
  12. 2020On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigationcited by 17
  13. 2016Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observationcited by 14

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