The Pix3D dataset is a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Source: http://pix3d.csail.mit.edu/ Image Source: http://pix3d.csail.mit.edu/

Dataset produced for the SAPIEN simulation environment. From the website: "PartNet-Mobility dataset is a collection of 2K articulated objects with motion annotations and rendernig material. The dataset powers research for generalizable computer vision and manipulation. The dataset is a continuation of ShapeNet and PartNet. "

DrivAerNet is a large-scale, high-fidelity CFD dataset of 3D industry-standard car shapes designed for data-driven aerodynamic design. It comprises 4000 high-quality 3D car meshes and their corresponding aerodynamic performance coefficients, alongside full 3D flow field information. It includes: - CFD Simulation Data: The raw dataset, including full 3D pressure, velocity fields, and wall-shear stresses, computed using 8-16 million mesh elements has a total size of $\sim$ 16TB. - Curated CFD Simulations: For ease of access and use, a streamlined version of the CFD simulation data is provided, refined to include key insights and data, reducing the size to $\sim$ 1TB. - 3D Car Meshes: A total of 4000 designs, showcasing a variety of conventional car shapes and emphasizing the impact of minor geometric modifications on aerodynamic efficiency. The 3D meshes and aerodynamic coefficients $\sim$ 84GB. - 2D slices include the car's wake in the $x$-direction and the symmetry plane in the $y$-direction $\sim$ 12GB.

From PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects: 5.1. Dataset Synthetic dataset. The synthetic 3D models we use for evaluation are from the PartNet-Mobility dataset [49, 27, 4], a large-scale dataset for articulated objects across 46 categories. We select instances across 10 categories to conduct our experiments. For each articulation state, we randomly sample 64-100 views covering the upper hemisphere of the object to simulate capturing in the real world. Then we render RGB images and acquire camera parameters and object masks using Blender [6] to create our training data. Real-world dataset. The real data we use for experiments is from the MultiScan dataset [25], scanning real-world indoor scenes with articulated objects in multiple states. We use the reconstructed mesh of an object in two states as ground truth for evaluation, and the real RGB frames as training data. From the project GitHub: We release both synthetic and real data shown in the paper here. Once downloaded, folders data and load should be put directly under the project directory. If you find it slow to download the data from our server, please try this OneDrive link instead. Source: PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects

📚 BlendNet The dataset contains $12k$ samples. To balance cost savings with data quality and scale, we manually annotated $2k$ samples and used GPT-4o to annotate the remaining $10k$ samples. For more details, please visit our GitHub repository

📚 CADBench CADBench is a comprehensive benchmark to evaluate the ability of LLMs to generate CAD scripts. It contains 500 simulated data samples and 200 data samples collected from online forums. For more details, please visit our GitHub repository

50K synthetic renders of the human foot, with surface normals, masks and keypoints.