mlsanityData to 7 Oct 2026

Depth-Independent Depth Completion via Least Square Estimation

arXiv 2203.03317 ↗First posted 7 Mar 2022cs.CV cs.ROcited by 0

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Abstract (arXiv)

The depth completion task aims to complete a per-pixel dense depth map from a sparse depth map. In this paper, we propose an efficient least square based depth-independent method to complete the sparse depth map utilizing the RGB image and the sparse depth map in two independent stages. In this way can we decouple the neural network and the sparse depth input, so that when some features of the sparse depth map change, such as the sparsity, our method can still produce a promising result. Moreover, due to the positional encoding and linear procession in our pipeline, we can easily produce a super-resolution dense depth map of high quality. We also test the generalization of our method on different datasets compared to some state-of-the-art algorithms. Experiments on the benchmark show that our method produces competitive performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 7 Mar 2022.

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19 of 30 references.

References · 19

  1. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 983
  2. 2018Pyramid Stereo Matching Networkcited by 486
  3. 2017VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorcited by 418
  4. 2018An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solutioncited by 302
  5. 2018PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsingcited by 224
  6. 2020Non-Local Spatial Propagation Network for Depth Completioncited by 194
  7. 2018BA-Net: Dense Bundle Adjustment Networkcited by 173
  8. 2017Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagecited by 159
  9. 2020MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learningcited by 157
  10. 2018Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Cameracited by 140
  11. 2018Learning Depth with Convolutional Spatial Propagation Networkcited by 135
  12. 2020DeepFactors: Real-Time Probabilistic Dense Monocular SLAMcited by 117
7 more
  1. 2018Deep Depth Completion of a Single RGB-D Imagecited by 110
  2. 2019CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completioncited by 110
  3. 2018Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Networkcited by 109
  4. 2017Learning Affinity via Spatial Propagation Networkscited by 79
  5. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
  6. 2018HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completioncited by 32
  7. 2019Depth Completion via Deep Basis Fittingcited by 23

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