WHU-Infra3D is a new large-scale multi-modal dataset and benchmark for 3D roadside infrastructure inventory, providing over 175k 2D boxes, thousands of 3D instances, and 181k annotations across five core tasks while exposing cross-city gaps and long-tailed defect vulnerabilities.
Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.
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Proposes the first light field-LiDAR semantic segmentation dataset and the Mlpfseg network, which improves mIoU by 1.71 over image-only and 2.38 over point-cloud-only baselines via feature completion and depth perception modules.
OSFENet performs one-shot edge detection on point clouds by learning scanner-specific data distributions using filtered-KNN surface patches and an RBF_DoS module, outperforming general-trained networks.
Delaunay Canopy uses Delaunay graphs as a geometric prior with region-wise curvature scoring to reconstruct accurate building wireframes from sparse and noisy airborne LiDAR point clouds.
SynthCity is a 367.9M point synthetic full-colour Mobile Laser Scanning point cloud with per-point labels from nine categories, generated in Blender for an urban environment.
A survey of deep learning architectures for 3D sensed data classification covering RGB-D, multi-view, volumetric and end-to-end methods along with datasets and future directions.
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Geometry-Aware Cross Modal Alignment for Light Field-LiDAR Semantic Segmentation
Proposes the first light field-LiDAR semantic segmentation dataset and the Mlpfseg network, which improves mIoU by 1.71 over image-only and 2.38 over point-cloud-only baselines via feature completion and depth perception modules.