# 3D Surface Reconstruction from Voxel-based Lidar Data

arXiv: https://arxiv.org/abs/1906.10515 · HTML: https://mlsanity.com/paper/1906.10515/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [ViiNeuS: Volumetric Initialization for Implicit Neural Surface reconstruction of urban scenes with limited image overlap](https://mlsanity.com/paper/2403.10344/) (2024)
- [Revisiting LiDAR Registration and Reconstruction: A Range Image Perspective](https://mlsanity.com/paper/2112.02779/) (2021)
- [Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function](https://mlsanity.com/paper/2202.13855/) (2022)

## Abstract (arXiv)

To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surrounding. In this paper, a 3D surface reconstruction algorithm from heterogeneous density 3D data is presented. The proposed method is based on a TSDF voxel-based representation, where an adaptive neighborhood kernel sourced on a Gaussian confidence evaluation is introduced. This enables to keep a good trade-off between the density of the reconstructed mesh and its accuracy. Experimental evaluations carried on both synthetic (CARLA) and real (KITTI) 3D data show a good performance compared to a state of the art method used for surface reconstruction.