ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes). Source: A review on deep learning techniques for 3D sensed data classification Image Source: ShapeNet: An Information-Rich 3D Model Repository

IntrA is an open-access 3D intracranial aneurysm dataset that makes the application of points-based and mesh-based classification and segmentation models available. This dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction. 103 3D models of entire brain vessels are collected by reconstructing scanned 2D MRA images of patients (the raw 2D MRA images are not published due to medical ethics). 1909 blood vessel segments are generated automatically from the complete models, including 1694 healthy vessel segments and 215 aneurysm segments for diagnosis. 116 aneurysm segments are divided and annotated manually by medical experts; the scale of each aneurysm segment is based on the need for a preoperative examination. Geodesic distance matrices are computed and included for each annotated 3D segment, because the expression of the geodesic distance is more accurate than Euclidean distance according to the shape of vessels. Source: https://github.com/intra3d2019/IntrA Image Source: https://github.com/intra3d2019/IntrA

Tasks. In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes. # Metric To assess the labeling performance, we rely on the commonly applied Jaccard Index or intersection-over-union (mIoU) metric over moving parts of the environment. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class. # Citation Citation. More information on the task and the metric, you can find in our publication related to the task: @article{chen2021ral, title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}}, author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss}, year={2021}, journal={IEEE Robotics and Automation Letters(RA-L)}, doi = {10.1109/LRA.2021.3093567} }

Intraoral 3D scans analysis is a fundamental aspect of Computer-Aided Dentistry (CAD) systems, playing a crucial role in various dental applications, including teeth segmentation, detection, labeling, and dental landmark identification. Accurate analysis of 3D dental scans is essential for orthodontic and prosthetic treatment planning, as it enables automated processing and reduces the need for manual adjustments by dental professionals. However, developing robust automated tools for these tasks remains a significant challenge due to the limited availability of high-quality public datasets and benchmarks. This article introduces Teeth3DS+, the first comprehensive public benchmark designed to advance the field of intraoral 3D scan analysis. Developed as part of the 3DTeethSeg 2022 and 3DTeethLand 2024 MICCAI challenges, Teeth3DS+ aims to drive research in teeth identification, segmentation, labeling, 3D modeling, and dental landmarks identification. The dataset includes at least 1,800 intraoral scans (containing 23,999 annotated teeth) collected from 900 patients, covering both upper and lower jaws separately. All data have been acquired and validated by experienced orthodontists and dental surgeons with over five years of expertise. Detailed instructions for accessing the dataset are available at https://crns-smartvision.github.io/teeth3ds

A dataset made of 3D image data and their embeddings to test TomoSAM

Workshop Tools Dataset Motivated by the need for a dataset that also includes inertial information about the objects, we contribute the following dataset. It contains 20 common workshop tools, and for each object: - a watertight triangular surface mesh; - a synthetic colored surface point-cloud; - ground truth inertial parameters; - ground truth part-level segmentation; and - a grasping reference frame. ## List of Objects - Adjustable Wrench - Bent Jaw Pliers - C Clamp - Electronic Caliper - Hacksaw - Machinist Hammer - Nut Screwdriver - Ruler - Socket Wrench - Vise Grip - Allen Key - Box Wrench - Clamp - File - Hammer - Measuring Tape - Pliers - Rubber Mallet - Screwdriver - Vise Clamp ## List of Files per Object Each object has its on dedicated folder containing the following files: - Frames.png : Picture of the point-cloud in point-cloud.ply with the mesh reference frames, the grasping reference frame and the centre of mass. The mesh reference frame is the frame all points are expressed relative to and the world "Origin" is written over its origin. The grasping reference frame was manually defined such as to express how a human worker would intuitively grasp the object. The centre of mass of the object is visualized as a purple ball. - Inertia.txt : Geometric and mass properties of the object as computed with the CAD software used to produce the triangular mesh. This file is not directly used by our software but can be more easily understood by humans. - Inertia.yaml : This file is read by our software to obtain the ground truth inertial properties as well as the transform that relates the grasping reference frame with respect to the mesh frame. - Materials.txt : Human-readable notes about the materials, and therefore the mass densities, used for the object parts. - mesh.ply : Binary millimiter-scaled triangular mesh with colored vertices and the following header: ply format binary_little_endian 1.0 comment SOLIDWORKS generated,length unit = millimeters element vertex 10913 property float x property float y property float z element face 21642 property uchar red property uchar green property uchar blue property uchar alpha property list uchar int vertex_indices end_header - point-cloud.ply : Colored and part-labeled point cloud with the following format header: ply format ascii 1.0 comment Created by Open3D element vertex 2000 property float32 x property float32 y property float32 z property float32 red property float32 green property float32 blue property uint8 segmentation end_header - reconstructed_mesh.ply : Triangular mesh reconstructed from the point-cloud in point-cloud.ply using the method described in the paper referenced below. Can be compared to the original mesh in mesh.ply to evaluate the quality of the reconstruction. ply format ascii 1.0 comment VCGLIB generated element vertex 256 property double x property double y property double z element face 500 property list uchar int vertex_indices end_header ## Citation If you used any part of this software in your work, please cite our paper: @inproceedings{Nadeau_PartSegForInertialIdent_2023, AUTHOR = {Philippe Nadeau AND Matthew Giamou AND Jonathan Kelly}, TITLE = { {The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects} }, BOOKTITLE = {Proceedings of the {IEEE} International Conference on Robotics and Automation {(ICRA'23})}, YEAR = {2023}, ADDRESS = {London, UK}, MONTH = {May}, DOI = {} }