# SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving

Source: [SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving](<https://www.sota2.com/research/paper/68fa677a1e209131cea16a6f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:16:13.371Z

Authors: Eren Erdal Aksoy, Saimir Baci, Selcuk Cavdar

Published: 2019-09-18T08:57:20.000Z

## Abstract

In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the road, i.e. drivable free-space, and vehicles in the scene by employing the Bird-Eye-View (BEV) image projection of the point cloud. To overcome the lack of annotated point cloud data, in particular for the road segments, we introduce an auto-labeling process which transfers automatically generated labels from the camera to LiDAR. We also explore the role of imagelike projection of LiDAR data in semantic segmentation by comparing BEV with spherical-front-view projection and show that SalsaNet is projection-agnostic. We perform quantitative and qualitative evaluations on the KITTI dataset, which demonstrate that the proposed SalsaNet outperforms other state-of-the-art semantic segmentation networks in terms of accuracy and computation time. Our code and data are publicly available at https://gitlab.com/aksoyeren/salsanet.git.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [LiDAR Semantic Segmentation on SemanticKITTI sequences 11 to 21 (test)](<https://www.sota2.com/research/sota/lidar-semantic-segmentation-on-semantickitti-sequences-11-to-21-test.md>): LiDAR Semantic Segmentation; SemanticKITTI sequences 11 to 21 (test); Car IoU: 87.5.

- [Semantic Segmentation on SemanticKITTI (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-semantickitti-test.md>): Semantic segmentation; SemanticKITTI (test); mIoU: 45.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.08291>)

[PDF](<https://arxiv.org/pdf/1909.08291.pdf>)