LIDAR Semantic Segmentation

The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers. Source: PointPainting: Sequential Fusion for 3D Object Detection

SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing. Source: Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation Image Source: https://github.com/PRBonn/semantic-kitti-api

ScribbleKITTI is a scribble-annotated dataset for LiDAR semantic segmentation.

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

SemanticSTF is an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3DSS under various adverse weather conditions. It contains 2,076 scans captured by a Velodyne HDL64 S3D LiDAR sensor from STF that cover various adverse weather conditions including 694 snowy, 637 dense-foggy, 631 light-foggy, and 114 rainy (all rainy LiDAR scans in STF). Source: 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds Image Source: 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds

WildScenes is a bi-modal benchmark dataset consisting of multiple large-scale, sequential traversals in natural environments, including semantic annotations in high-resolution 2D images and dense 3D LiDAR point clouds, and accurate 6-DoF pose information. The data is (1) trajectory-centric with accurate localization and globally aligned point clouds, (2) calibrated and synchronized to support bi-modal training and inference, and (3) containing different natural environments over 6 months to support research on domain adaptation. We introduce benchmarks on 2D and 3D semantic segmentation and evaluate a variety of recent deep-learning techniques to demonstrate the challenges in semantic segmentation in natural environments. We propose train-val-test splits for standard benchmarks as well as domain adaptation benchmarks and utilize an automated split generation technique to ensure the balance of class label distributions. The WildScenes benchmark webpage is https://csiro-robotics.github.io/WildScenes, and the data is publicly available at https://data.csiro.au/collection/csiro:61541 .

SeMantic InDustry (S.MID) is a dataset designed to advance the field of LiDAR semantic segmentation, specifically for robotic applications and large-scale industrial scene. The dataset is based on a hybrid-solid LiDAR (Livox Mid-360). To create S.MID, researchers used an industrial robot to collect a total of 38,904 frames of LiDAR data at a rate of 10 Hz across various substations. The LiDAR point clouds are annotated into 25 categories under professional guidance (14 categories for single frame segmentation task) .

This is a dataset with curb annotations by using 3D LiDAR data and we build this dataset based on the SemanticKITTI dataset.

A cross-city UDA benchmark built upon nuScenes.

UAV Laser Scanning data collected over neotropical forest (Paracou French Guiana). Four flights conducted over one ha plot in 2021 and 2022. Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction. Predicted values using SOUL model (see ref below) for a single UAV-LS acquisition are provided as separate file.