mlsanityData to 14 Oct 2026

BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging Bird's-Eye-View in Dynamic Scenarios

arXiv 2306.11598 ↗First posted 20 Jun 2023cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Depth estimation is a cornerstone of perception in autonomous driving and robotic systems. The considerable cost and relatively sparse data acquisition of LiDAR systems have led to the exploration of cost-effective alternatives, notably, self-supervised depth estimation. Nevertheless, current self-supervised depth estimation methods grapple with several limitations: (1) the failure to adequately leverage informative multi-camera views. (2) the limited capacity to handle dynamic objects effectively. To address these challenges, we present BEVScope, an innovative approach to self-supervised depth estimation that harnesses Bird's-Eye-View (BEV) features. Concurrently, we propose an adaptive loss function, specifically designed to mitigate the complexities associated with moving objects. Empirical evaluations conducted on the Nuscenes dataset validate our approach, demonstrating competitive performance. Code will be released at https://github.com/myc634/BEVScope.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 36 references that are in the map. First posted on arXiv 20 Jun 2023.

Builds on

34 of 36 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  7. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  8. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
  9. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  10. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  11. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 713
  12. 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 658
22 more
  1. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 572
  2. 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 552
  3. 2018Pyramid Stereo Matching Networkcited by 489
  4. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 439
  5. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  6. 2022PETRv2: A Unified Framework for 3D Perception from Multi-Camera Imagescited by 352
  7. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  8. 2022BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detectioncited by 341
  9. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  10. 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
  11. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
  12. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
  13. 2022Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detectioncited by 178
  14. 2022Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusioncited by 162
  15. 2022PolarFormer: Multi-camera 3D Object Detection with Polar Transformercited by 147
  16. 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
  17. 2022BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereocited by 90
  18. 2020Towards Better Generalization: Joint Depth-Pose Learning without PoseNetcited by 85
  19. 2022SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimationcited by 76
  20. 2021Full Surround Monodepth from Multiple Camerascited by 41
  21. 2019Moving Indoor: Unsupervised Video Depth Learning in Challenging Environmentscited by 31
  22. 2022Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimationcited by 16

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2024SDGE: Stereo Guided Depth Estimation for 360^circ Camera Setscited by 5
  2. 2023EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformercited by 2
  3. 2025PFDepth: Heterogeneous Pinhole-Fisheye Joint Depth Estimation via Distortion-aware Gaussian-Splatted Volumetric Fusioncited by 1