mlsanityData to 7 Oct 2026

RigNet: Repetitive Image Guided Network for Depth Completion

arXiv 2107.13802 ↗First posted 29 Jul 2021cs.CVcited by 80

Not on the map. Why?

Abstract (arXiv)

Depth completion deals with the problem of recovering dense depth maps from sparse ones, where color images are often used to facilitate this task. Recent approaches mainly focus on image guided learning frameworks to predict dense depth. However, blurry guidance in the image and unclear structure in the depth still impede the performance of the image guided frameworks. To tackle these problems, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values. Specifically, the repetition is embodied in both the image guidance branch and depth generation branch. In the former branch, we design a repetitive hourglass network to extract discriminative image features of complex environments, which can provide powerful contextual instruction for depth prediction. In the latter branch, we introduce a repetitive guidance module based on dynamic convolution, in which an efficient convolution factorization is proposed to simultaneously reduce its complexity and progressively model high-frequency structures. Extensive experiments show that our method achieves superior or competitive results on KITTI benchmark and NYUv2 dataset.

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). Builds on 50 of its 66 references that are in the map. First posted on arXiv 29 Jul 2021.

Builds on

50 of 66 references.

References · 50

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  7. 2017Squeeze-and-Excitation Networkscited by 4,763
  8. 2016Pyramid Scene Parsing Networkcited by 2,472
  9. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  10. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  11. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  12. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
38 more
  1. 2018Path Aggregation Network for Instance Segmentationcited by 796
  2. 2019Selective Kernel Networkscited by 401
  3. 2016Virtual Worlds as Proxy for Multi-Object Tracking Analysiscited by 340
  4. 2017Sparsity Invariant CNNscited by 325
  5. 2018Context Encoding for Semantic Segmentationcited by 289
  6. 2019NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detectioncited by 289
  7. 2020DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolutioncited by 222
  8. 2020Non-Local Spatial Propagation Network for Depth Completioncited by 194
  9. 2017Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagecited by 159
  10. 2019Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentationcited by 141
  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. 2018DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Imagecited by 129
  14. 2019Learning Guided Convolutional Network for Depth Completioncited by 111
  15. 2018Deep Depth Completion of a Single RGB-D Imagecited by 110
  16. 2019CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completioncited by 110
  17. 2019Depth Completion from Sparse LiDAR Data with Depth-Normal Constraintscited by 108
  18. 2018In Defense of Classical Image Processing: Fast Depth Completion on the CPUcited by 96
  19. 2019Sparse and noisy LiDAR completion with RGB guidance and uncertaintycited by 79
  20. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
  21. 2020Learning Joint 2D-3D Representations for Depth Completioncited by 71
  22. 2020Adaptive Context-Aware Multi-Modal Network for Depth Completioncited by 69
  23. 2019Dense Depth Posterior (DDP) from Single Image and Sparse Rangecited by 57
  24. 2020VisualEchoes: Spatial Image Representation Learning through Echolocationcited by 54
  25. 2018Confidence Propagation through CNNs for Guided Sparse Depth Regressioncited by 52
  26. 2020FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completioncited by 45
  27. 2019CBNet: A Novel Composite Backbone Network Architecture for Object Detectioncited by 44
  28. 2019Depth Coefficients for Depth Completioncited by 41
  29. 2021Depth Completion with Twin Surface Extrapolation at Occlusion Boundariescited by 41
  30. 2020Channel Attention based Iterative Residual Learning for Depth Map Super-Resolutioncited by 40
  31. 20173D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detectioncited by 35
  32. 2020Deformable spatial propagation network for depth completioncited by 30
  33. 2019Indoor Depth Completion with Boundary Consistency and Self-Attentioncited by 29
  34. 2018Deep Convolutional Compressed Sensing for LiDAR Depth Completioncited by 27
  35. 2021Beyond Image to Depth: Improving Depth Prediction using Echoescited by 24
  36. 2021Depth Completion using Plane-Residual Representationcited by 24
  37. 2021Bayesian Deep Basis Fitting for Depth Completion with Uncertaintycited by 23
  38. 2018Real-Time Dense Mapping for Self-driving Vehicles using Fisheye Camerascited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 10; 2023: 18; 2024: 26; 2025: 22; 2026: 4.

80 papers.

Sort:

By area:

  1. 2023Equivariant Multi-Modality Image Fusioncited by 98
  2. 2022PanoFormer: Panorama Transformer for Indoor 360 Depth Estimationcited by 94
  3. 2023CompletionFormer: Depth Completion with Convolutions and Vision Transformerscited by 92
  4. 2024Bilateral Propagation Network for Depth Completioncited by 59
  5. 2024Context and Geometry Aware Voxel Transformer for Semantic Scene Completioncited by 50
  6. 2023LRRU: Long-short Range Recurrent Updating Networks for Depth Completioncited by 48
  7. 2024MonoOcc: Digging into Monocular Semantic Occupancy Predictioncited by 43
  8. 2024Tri-Perspective View Decomposition for Geometry-Aware Depth Completioncited by 42
  9. 2022Deep Depth Completion from Extremely Sparse Data: A Surveycited by 33
  10. 2022DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completioncited by 27
  11. 2023SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolutioncited by 27
  12. 2024Text2LiDAR: Text-guided LiDAR Point Cloud Generation via Equirectangular Transformercited by 26
38 more
  1. 2022Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completioncited by 23
  2. 2022SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completioncited by 23
  3. 2024OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterationscited by 23
  4. 2025Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimationcited by 20
  5. 2022Consistent Direct Time-of-Flight Video Depth Super-Resolutioncited by 16
  6. 2023Learning a Depth Covariance Functioncited by 16
  7. 2024Deep Height Decoupling for Precise Vision-based 3D Occupancy Predictioncited by 16
  8. 2023Learnable Differencing Center for Nighttime Depth Perceptioncited by 14
  9. 2024DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolutioncited by 14
  10. 2025Distilling Monocular Foundation Model for Fine-grained Depth Completioncited by 14
  11. 2023FDCT: Fast Depth Completion for Transparent Objectscited by 12
  12. 2023Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completioncited by 12
  13. 2023TULIP: Transformer for Upsampling of LiDAR Point Cloudscited by 12
  14. 2024Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completioncited by 12
  15. 2025VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detectioncited by 12
  16. 2023RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completioncited by 11
  17. 2024DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domaincited by 11
  18. 2025DuCos: Duality Constrained Depth Super-Resolution via Foundation Modelcited by 11
  19. 2025VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Predictioncited by 9
  20. 2023AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completioncited by 8
  21. 2023NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completioncited by 8
  22. 2024RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methodscited by 8
  23. 2023RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completioncited by 7
  24. 2022Learning an Efficient Multimodal Depth Completion Modelcited by 6
  25. 2024Scene Prior Filtering for Depth Super-Resolutioncited by 5
  26. 2024Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinementcited by 5
  27. 2025Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flowcited by 5
  28. 2025Radar-Guided Polynomial Fitting for Metric Depth Estimationcited by 5
  29. 2025Event-Driven Dynamic Scene Depth Completioncited by 5
  30. 2025Structure-Aware Radar-Camera Depth Estimationcited by 5
  31. 2022UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completioncited by 4
  32. 2023Revisiting Deformable Convolution for Depth Completioncited by 4
  33. 2024DeCoTR: Enhancing Depth Completion with 2D and 3D Attentionscited by 4
  34. 2025SpatioTemporal Difference Network for Video Depth Super-Resolutioncited by 4
  35. 2024Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mappingcited by 3
  36. 2024Diff-Reg v1: Diffusion Matching Model for Registration Problemcited by 3
  37. 2026Large Depth Completion Model from Sparse Observationscited by 3
  38. 2022SparseFormer: Attention-based Depth Completion Networkcited by 2

50 most cited of 80 All 80 (JSON)