Point Cloud Segmentation

PointCloud-C is the very first test-suite for point cloud robustness analysis under corruptions. - Two sets: ModelNet-C for point cloud classification and ShapeNet-C for part segmentation. - Real-world corruption sources, ranging from object-, senor-, and processing-levels. - Seven types of corruptions, each with five severity levels. - Benchmark with more than 20 point cloud recognition algorithms. - Methods ranging from architecture design, augmentations, and pre-training.

A Multi-Task 4D Radar-Camera Fusion Dataset for Autonomous Driving on Water Surfaces description of the dataset * WaterScenes, the first multi-task 4D radar-camera fusion dataset on water surfaces, which offers data from multiple sensors, including a 4D radar, monocular camera, GPS, and IMU. It can be applied in multiple tasks, such as object detection, instance segmentation, semantic segmentation, free-space segmentation, and waterline segmentation. * Our dataset covers diverse time conditions (daytime, nightfall, night), lighting conditions (normal, dim, strong), weather conditions (sunny, overcast, rainy, snowy) and waterway conditions (river, lake, canal, moat). An information list is also offered for retrieving specific data for experiments under different conditions. * We provide 2D box-level and pixel-level annotations for camera images, and 3D point-level annotations for radar point clouds. We also offer precise timestamps for the synchronization of different sensors, as well as intrinsic and extrinsic parameters. * We provide a toolkit for radar point clouds that includes: pre-processing, labeling, projection and visualization, assisting researchers in processing and analyzing our dataset.

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 .