# Deterministic Guided LiDAR Depth Map Completion

arXiv: https://arxiv.org/abs/2106.07256 · HTML: https://mlsanity.com/paper/2106.07256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [Sparsity Invariant CNNs](https://mlsanity.com/paper/1708.06500/) (2017)
- [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)
- [Learning Guided Convolutional Network for Depth Completion](https://mlsanity.com/paper/1908.01238/) (2019)
- [In Defense of Classical Image Processing: Fast Depth Completion on the CPU](https://mlsanity.com/paper/1802.00036/) (2018)
- [The Fast Bilateral Solver](https://mlsanity.com/paper/1511.03296/) (2015)
- [Learning Joint 2D-3D Representations for Depth Completion](https://mlsanity.com/paper/2012.12402/) (2020)
- [Fast Guided Filter](https://mlsanity.com/paper/1505.00996/) (2015)
- [High-resolution LIDAR-based Depth Mapping using Bilateral Filter](https://mlsanity.com/paper/1606.05614/) (2016)
- [Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor](https://mlsanity.com/paper/2006.14374/) (2020)
- [The Domain Transform Solver](https://mlsanity.com/paper/1805.04590/) (2018)
- [Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction](https://mlsanity.com/paper/1907.10148/) (2019)

## Built on by (most cited first, 2 of 2)

- [Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion](https://mlsanity.com/paper/2203.09855/) (2022)
- [FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving](https://mlsanity.com/paper/2304.10719/) (2023)

## Abstract (arXiv)

Accurate dense depth estimation is crucial for autonomous vehicles to analyze their environment. This paper presents a non-deep learning-based approach to densify a sparse LiDAR-based depth map using a guidance RGB image. To achieve this goal the RGB image is at first cleared from most of the camera-LiDAR misalignment artifacts. Afterward, it is over segmented and a plane for each superpixel is approximated. In the case a superpixel is not well represented by a plane, a plane is approximated for a convex hull of the most inlier. Finally, the pinhole camera model is used for the interpolation process and the remaining areas are interpolated. The evaluation of this work is executed using the KITTI depth completion benchmark, which validates the proposed work and shows that it outperforms the state-of-the-art non-deep learning-based methods, in addition to several deep learning-based methods.