# Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching

arXiv: https://arxiv.org/abs/2210.01436 · HTML: https://mlsanity.com/paper/2210.01436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 24 of 24)

- [Sparsity Invariant CNNs](https://mlsanity.com/paper/1708.06500/) (2017)
- [Non-Local Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2007.10042/) (2020)
- [Hierarchical Neural Architecture Search for Deep Stereo Matching](https://mlsanity.com/paper/2010.13501/) (2020)
- [Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera](https://mlsanity.com/paper/1807.00275/) (2018)
- [DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image](https://mlsanity.com/paper/1812.00488/) (2018)
- [CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion](https://mlsanity.com/paper/1911.05377/) (2019)
- [Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints](https://mlsanity.com/paper/1910.06727/) (2019)
- [Unsupervised Depth Completion from Visual Inertial Odometry](https://mlsanity.com/paper/1905.08616/) (2019)
- [Sparse and noisy LiDAR completion with RGB guidance and uncertainty](https://mlsanity.com/paper/1902.05356/) (2019)
- [Learning Joint 2D-3D Representations for Depth Completion](https://mlsanity.com/paper/2012.12402/) (2020)
- [Adaptive Context-Aware Multi-Modal Network for Depth Completion](https://mlsanity.com/paper/2008.10833/) (2020)
- [Dense Depth Posterior (DDP) from Single Image and Sparse Range](https://mlsanity.com/paper/1901.10034/) (2019)
- [Confidence Propagation through CNNs for Guided Sparse Depth Regression](https://mlsanity.com/paper/1811.01791/) (2018)
- [FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion](https://mlsanity.com/paper/2012.08270/) (2020)
- [DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion](https://mlsanity.com/paper/1902.00761/) (2019)
- [Noise-Aware Unsupervised Deep Lidar-Stereo Fusion](https://mlsanity.com/paper/1904.03868/) (2019)
- [PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching](https://mlsanity.com/paper/2103.07094/) (2021)
- [LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry](https://mlsanity.com/paper/1804.05178/) (2018)
- [Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation](https://mlsanity.com/paper/2103.12964/) (2021)
- [Embedded real-time stereo estimation via Semi-Global Matching on the GPU](https://mlsanity.com/paper/1610.04121/) (2016)
- [SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation](https://mlsanity.com/paper/2008.09346/) (2020)
- [Semantically Guided Depth Upsampling](https://mlsanity.com/paper/1608.00753/) (2016)
- [DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles](https://mlsanity.com/paper/2007.02438/) (2020)
- [Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor](https://mlsanity.com/paper/2006.14374/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Reinforcement Learning Method for Environments with Stochastic Variables: Post-Decision Proximal Policy Optimization with Dual Critic Networks](https://mlsanity.com/paper/2504.05150/) (2025)

## Abstract (arXiv)

We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two limiations. (i) They assume the given sparse depth map is accurately aligned to the input image, whereas the alignment is difficult to achieve in practice. (ii) They have limited accuracy in the long range because the depth is estimated by pixel disparity. To solve the abovementioned limitations, we propose selective stereo matching (SSM) that searches the most appropriate depth value for each image pixel from its neighborly projected LiDAR points based on an energy minimization framework. This depth selection approach can handle any type of mis-projection. Moreover, SSM has an advantage in terms of long-range depth accuracy because it directly uses the LiDAR measurement rather than the depth acquired from the stereo. SSM is a discrete process; thus, we apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to generate a continuous depth map while preserving depth discontinuity across object boundaries. Experimentally, compared with the previous state-of-the-art stereo-aided depth completion, the proposed method reduced the mean absolute error (MAE) of the depth estimation to 0.65 times and demonstrated approximately twice more accurate estimation in the long range. Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0.34-0.93 times from previous depth completion methods.