# A Surface Geometry Model for LiDAR Depth Completion arXiv: https://arxiv.org/abs/2104.08466 ยท HTML: https://mlsanity.com/paper/2104.08466/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 43 references that are in the map. - First posted on arXiv 17 Apr 2021. ## Builds on (most cited first, 25 of 25) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016) - [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017) - [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018) - [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) - [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) - [Deep Depth Completion of a Single RGB-D Image](https://mlsanity.com/paper/1803.09326/) (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) - [In Defense of Classical Image Processing: Fast Depth Completion on the CPU](https://mlsanity.com/paper/1802.00036/) (2018) - [Unsupervised Depth Completion from Visual Inertial Odometry](https://mlsanity.com/paper/1905.08616/) (2019) - [The Fast Bilateral Solver](https://mlsanity.com/paper/1511.03296/) (2015) - [Sparse and noisy LiDAR completion with RGB guidance and uncertainty](https://mlsanity.com/paper/1902.05356/) (2019) - [Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation](https://mlsanity.com/paper/1808.00769/) (2018) - [Learning Joint 2D-3D Representations for Depth Completion](https://mlsanity.com/paper/2012.12402/) (2020) - [Dense Depth Posterior (DDP) from Single Image and Sparse Range](https://mlsanity.com/paper/1901.10034/) (2019) - [Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End](https://mlsanity.com/paper/2006.03349/) (2020) - [Confidence Propagation through CNNs for Guided Sparse Depth Regression](https://mlsanity.com/paper/1811.01791/) (2018) - [Depth Coefficients for Depth Completion](https://mlsanity.com/paper/1903.05421/) (2019) - [Propagating Confidences through CNNs for Sparse Data Regression](https://mlsanity.com/paper/1805.11913/) (2018) - [Deep Convolutional Compressed Sensing for LiDAR Depth Completion](https://mlsanity.com/paper/1803.08949/) (2018) - [Semantically Guided Depth Upsampling](https://mlsanity.com/paper/1608.00753/) (2016) - [Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor](https://mlsanity.com/paper/2006.14374/) (2020) ## Built on by (most cited first, 18 of 18) - [VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles](https://mlsanity.com/paper/2111.12083/) (2021) - [Bilateral Propagation Network for Depth Completion](https://mlsanity.com/paper/2403.11270/) (2024) - [LRRU: Long-short Range Recurrent Updating Networks for Depth Completion](https://mlsanity.com/paper/2310.08956/) (2023) - [FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding](https://mlsanity.com/paper/2109.03787/) (2021) - [Sparsity Agnostic Depth Completion](https://mlsanity.com/paper/2212.00790/) (2022) - [Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization](https://mlsanity.com/paper/2312.09254/) (2023) - [Unsupervised confidence for LiDAR depth maps and applications](https://mlsanity.com/paper/2210.03118/) (2022) - [A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2109.08224/) (2021) - [CU-Net: LiDAR Depth-Only Completion With Coupled U-Net](https://mlsanity.com/paper/2210.14898/) (2022) - [Multi-View Guided Multi-View Stereo](https://mlsanity.com/paper/2210.11467/) (2022) - [SMORE: Simultaneous Map and Object REconstruction](https://mlsanity.com/paper/2406.13896/) (2024) - [RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion](https://mlsanity.com/paper/2404.09290/) (2024) - [Learning Inverse Laplacian Pyramid for Progressive Depth Completion](https://mlsanity.com/paper/2502.07289/) (2025) - [Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion](https://mlsanity.com/paper/2508.04984/) (2025) - [Gaussian Belief Propagation Network for Depth Completion](https://mlsanity.com/paper/2601.21291/) (2026) - [A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2207.05888/) (2022) - [WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research](https://mlsanity.com/paper/2402.19059/) (2024) - [Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereo](https://mlsanity.com/paper/2507.19738/) (2025) ## Abstract (arXiv) LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame, although only sparse LiDAR points are available. Most of the existing state-of-the-art solutions are based on deep neural networks, which need a large amount of data and heavy computations for training the models. In this letter, a novel non-learning depth completion method is proposed by exploiting the local surface geometry that is enhanced by an outlier removal algorithm. The proposed surface geometry model is inspired by the observation that most pixels with unknown depth have a nearby LiDAR point. Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error. The residual error is calculated by using a derived equation with several physical parameters as input, including the known camera intrinsic parameters, estimated normal vector, and offset distance on the image plane. The proposed method is further enhanced by an outlier removal algorithm that is designed to remove incorrectly mapped LiDAR points from occluded regions. On KITTI dataset, the proposed solution achieves the best error performance among all existing non-learning methods and is comparable to the best self-supervised learning method and some supervised learning methods. Moreover, since outlier points from occluded regions is a commonly existing problem, the proposed outlier removal algorithm is a general preprocessing step that is applicable to many robotic systems with both camera and LiDAR sensors.