Datasets › Paris-Lille-3D

Paris-Lille-3D

Introduced by Xavier Roynard et al. in Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification archive 2025-07-28

The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille).

Source: Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification Image Source: https://npm3d.fr/paris-lille-3d

Homepage Benchmarks Papers JSON

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

		First row (archive order)	Paper	Code
LIDAR Semantic Segmentation	Paris-Lille-3D	FKAConv mIOU 0.827	FKAConv: Feature-Kernel Alignment for Point Cloud Convolution	valeoai/FKAConv	9	Compare

Papers archive 2025-07-28

7 shown of 7 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 15. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

			Date	Samples run Syntology
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo	1	2	26 Oct 2024	not harvested
Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks	0	1	28 Mar 2021	not harvested
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling	1	1	17 Dec 2020	not harvested
FKAConv: Feature-Kernel Alignment for Point Cloud Convolution	1	1	9 Apr 2020	not harvested
KPConv: Flexible and Deformable Convolution for Point Clouds	10	1	18 Apr 2019	ran 5 of 12 samples (7 unverified; 3 pointer-only for licence)
ConvPoint: Continuous Convolutions for Point Cloud Processing	1	2	4 Apr 2019	ran 4 of 4 samples (0 unverified; 4 pointer-only for licence)
Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification	0	1	30 Nov 2017	not harvested

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

Semantic Segmentation Few-Shot Learning 3D Semantic Segmentation LIDAR Semantic Segmentation

License archive 2025-07-28

CC BY-NC-ND 3.0 FR

Modalities archive 2025-07-28

Images

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

Paris-Lille-3D

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections