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Learning Joint 2D-3D Representations for Depth Completion

arXiv 2012.12402 ↗First posted 22 Dec 2020cs.CVcited by 71

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Abstract (arXiv)

In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal, we design a simple yet effective neural network block that learns to extract joint 2D and 3D features. Specifically, the block consists of two domain-specific sub-networks that apply 2D convolution on image pixels and continuous convolution on 3D points, with their output features fused in image space. We build the depth completion network simply by stacking the proposed block, which has the advantage of learning hierarchical representations that are fully fused between 2D and 3D spaces at multiple levels. We demonstrate the effectiveness of our approach on the challenging KITTI depth completion benchmark and show that our approach outperforms the state-of-the-art.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 15; 2023: 10; 2024: 15; 2025: 7; 2026: 1.

71 papers.

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  1. 2021PENet: Towards Precise and Efficient Image Guided Depth Completioncited by 146
  2. 2021RigNet: Repetitive Image Guided Network for Depth Completioncited by 80
  3. 2021GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Drivingcited by 75
  4. 2021RGB-D Local Implicit Function for Depth Completion of Transparent Objectscited by 64
  5. 2021Unsupervised Depth Completion with Calibrated Backprojection Layerscited by 61
  6. 2024Bilateral Propagation Network for Depth Completioncited by 59
  7. 2024Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusioncited by 47
  8. 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
  9. 2024Tri-Perspective View Decomposition for Geometry-Aware Depth Completioncited by 42
  10. 2021Depth Completion with Twin Surface Extrapolation at Occlusion Boundariescited by 41
  11. 2021Learning Topology from Synthetic Data for Unsupervised Depth Completioncited by 38
  12. 2022Deep Depth Completion from Extremely Sparse Data: A Surveycited by 33
38 more
  1. 2021An Adaptive Framework for Learning Unsupervised Depth Completioncited by 30
  2. 2022DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Imagecited by 26
  3. 2024Test-Time Adaptation for Depth Completioncited by 26
  4. 2021Depth Completion using Plane-Residual Representationcited by 24
  5. 2022Monitored Distillation for Positive Congruent Depth Completioncited by 24
  6. 2021Bayesian Deep Basis Fitting for Depth Completion with Uncertaintycited by 23
  7. 2022SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completioncited by 23
  8. 2022GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNscited by 23
  9. 2024Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpaintingcited by 23
  10. 2021A Surface Geometry Model for LiDAR Depth Completioncited by 18
  11. 2021DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Networkcited by 17
  12. 2021Robust Depth Completion with Uncertainty-Driven Loss Functionscited by 16
  13. 2021Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimationcited by 15
  14. 2025Distilling Monocular Foundation Model for Fine-grained Depth Completioncited by 14
  15. 2021Advancing Self-supervised Monocular Depth Learning with Sparse LiDARcited by 13
  16. 2022TODE-Trans: Transparent Object Depth Estimation with Transformercited by 13
  17. 2022Depth Completion using Geometry-Aware Embeddingcited by 12
  18. 2023Semantic Scene Completion with Cleaner Selfcited by 12
  19. 2024CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimationcited by 12
  20. 2024Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completioncited by 12
  21. 2023RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completioncited by 11
  22. 2021S3: Learnable Sparse Signal Superdensity for Guided Depth Estimationcited by 10
  23. 2023Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completioncited by 10
  24. 2023Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedbackcited by 10
  25. 2023FinnWoodlands Datasetcited by 8
  26. 2023Active Stereo Without Pattern Projectorcited by 8
  27. 2023NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completioncited by 8
  28. 2024All-day Depth Completioncited by 8
  29. 2024GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsamplingcited by 7
  30. 2022Towards Domain-agnostic Depth Completioncited by 6
  31. 2022Learning an Efficient Multimodal Depth Completion Modelcited by 6
  32. 2022SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completioncited by 5
  33. 2024Scale Propagation Network for Generalizable Depth Completioncited by 5
  34. 2025Event-Driven Dynamic Scene Depth Completioncited by 5
  35. 2023Revisiting Deformable Convolution for Depth Completioncited by 4
  36. 2024DeCoTR: Enhancing Depth Completion with 2D and 3D Attentionscited by 4
  37. 2025Diffusion Distillation With Direct Preference Optimization For Efficient 3D LiDAR Scene Completioncited by 4
  38. 2021Similarity-Aware Fusion Network for 3D Semantic Segmentationcited by 3

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