# ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception

Source: [ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception](<https://www.sota2.com/research/paper/68fa73761e209131cea5cb0b>)

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

Data updated: 2025-10-26T02:50:15.439Z

Authors: Jules Sanchez, Louis Soum-Fontez, Jean-Emmanuel Deschaud, Francois Goulette

Published: 2023-10-25T10:45:38.000Z

## Abstract

LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. %Exploiting this information for perception is interesting as the amount of available data increases. As the performance of various LiDAR perception tasks has improved, generalizations to new environments and sensors has emerged to test these optimized models in real-world conditions.   This paper provides a novel dataset, ParisLuco3D, specifically designed for cross-domain evaluation to make it easier to evaluate the performance utilizing various source datasets. Alongside the dataset, online benchmarks for LiDAR semantic segmentation, LiDAR object detection, and LiDAR tracking are provided to ensure a fair comparison across methods.   The ParisLuco3D dataset, evaluation scripts, and links to benchmarks can be found at the following website:https://npm3d.fr/parisluco3d

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.16542>)

[PDF](<https://arxiv.org/pdf/2310.16542.pdf>)