mlsanityData to 7 Oct 2026

Deep Convolutional Compressed Sensing for LiDAR Depth Completion

arXiv 1803.08949 ↗First posted 23 Mar 2018cs.CVcited by 27

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Abstract (arXiv)

In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points. We use techniques from compressed sensing and the recently developed Alternating Direction Neural Networks (ADNNs) to create a deep recurrent auto-encoder for this task. Our architecture internally performs an algorithm for extracting multi-level convolutional sparse codes from the input which are then used to make a prediction. Our results demonstrate that with only two layers and 1800 parameters we are able to out perform all previously published results, including deep networks with orders of magnitude more parameters.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Mar 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 5; 2023: 4; 2024: 6; 2025: 0; 2026: 2.

27 papers.

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  1. 2021RigNet: Repetitive Image Guided Network for Depth Completioncited by 80
  2. 2021Unsupervised Depth Completion with Calibrated Backprojection Layerscited by 61
  3. 2024Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusioncited by 47
  4. 2021Learning Topology from Synthetic Data for Unsupervised Depth Completioncited by 38
  5. 2022Deep Depth Completion from Extremely Sparse Data: A Surveycited by 33
  6. 2021An Adaptive Framework for Learning Unsupervised Depth Completioncited by 30
  7. 2022Monitored Distillation for Positive Congruent Depth Completioncited by 24
  8. 2021Bayesian Deep Basis Fitting for Depth Completion with Uncertaintycited by 23
  9. 2022Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completioncited by 23
  10. 2022SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completioncited by 23
  11. 2021A Surface Geometry Model for LiDAR Depth Completioncited by 18
  12. 2023RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completioncited by 11
15 more
  1. 2024Distilling Diffusion Models to Efficient 3D LiDAR Scene Completioncited by 10
  2. 2024RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methodscited by 8
  3. 2024All-day Depth Completioncited by 8
  4. 2022CU-Net: LiDAR Depth-Only Completion With Coupled U-Netcited by 6
  5. 2021PDC: Piecewise Depth Completion utilizing Superpixelscited by 5
  6. 2023Learning to Estimate Two Dense Depths from LiDAR and Event Datacited by 5
  7. 2021DVMN: Dense Validity Mask Network for Depth Completioncited by 3
  8. 2021Self-Supervised Depth Completion for Active Stereocited by 3
  9. 2023Prior based Sampling for Adaptive LiDARcited by 2
  10. 2024RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusioncited by 2
  11. 2021Reframing Neural Networks: Deep Structure in Overcomplete Representationscited by 1
  12. 2024Progressive Depth Decoupling and Modulating for Flexible Depth Completioncited by 1
  13. 2023Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistencycited by 0
  14. 2026PTC-Depth: Pose-Refined Monocular Depth Estimation with Temporal Consistencycited by 0
  15. 2026Depth-to-Image Synthesis-Driven Generative Unguided Depth Completioncited by 0