This repository contains ShapeNetCore (v2), a subset of ShapeNet.ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in WordNet 3.0.
Please see DATA.md for details about the data. If you use ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that… See the full description on the dataset page: https://huggingface.co/datasets/ShapeNet/ShapeNetCore.

Dataset

This dataset was created by Jeremy26

Contents

ShapeNet Part Dataset is a dataset designed for 3D part-based segmentation, derived from the ShapeNetCore dataset. It contains 3D models of objects with annotations at the part level. Each object is represented by a point cloud (XYZ coordinates) along with part labels that indicate the specific part of the object. The dataset includes expert-verified segmentation labels for improved accuracy and usability.

It is ideal for training machine learning models in tasks such as semantic segmentation and 3D shape analysis.

This repository contains archives (zip files) for PartNet, a subset of ShapeNet with part annotations. The PartNet prerelease v0 (March 29, 2019) consists of the following:

PartNet v0 annotations (meshes, point clouds, and visualizations) in chunks: data_v0_chunk.zip (302MB), data_v0_chunk.z01-z10 (10GB each) HDF5 files for the semantic segmentation task (Sec 5.1 of PartNet paper): sem_seg_h5.zip (8GB) HDF5 files for the instance segmentation task (Sec 5.3 of PartNet paper): ins_seg_h5.zip… See the full description on the dataset page: https://huggingface.co/datasets/ShapeNet/PartNet-archive.

This repository contains ShapeNetCore (v2) in GLB format, a subset of ShapeNet.ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in WordNet 3.0.
If you use ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by… See the full description on the dataset page: https://huggingface.co/datasets/ShapeNet/shapenetcore-glb.

This dataset is a subset of the SHAPENET dataset in order to play with some of the data on kaggle

This repository contains ShapeNetCore (v2) in GLTF format, a subset of ShapeNet.ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in WordNet 3.0.
If you use ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by… See the full description on the dataset page: https://huggingface.co/datasets/ShapeNet/shapenetcore-gltf.

Dataset

This dataset was created by GABRIELE SCOGNAMIGLIO

Contents

ShapeNet

This repository contains archives (zip files) for ShapeNetSem, a subset of ShapeNet richly annotated with physical attributes. Please see DATA.md for details about the data. If you use ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions. If you use this data, please cite the main ShapeNet technical report and the… See the full description on the dataset page: https://huggingface.co/datasets/ShapeNet/ShapeNetSem-archive.

ShapeNet

Paper: https://arxiv.org/pdf/1512.03012 Files in this repo:

shapenetcore_partanno_segmentation_benchmark_v0_normal.zip - This is a drop-in replacement for https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip (which no longer exists) shapenetcore_partanno_segmentation_benchmark_v0_normal_npz.zip - This is a larger version (double the file size) taken from https://www.kaggle.com/datasets/mitkir/shapenet/data. It includes an… See the full description on the dataset page: https://huggingface.co/datasets/cminst/ShapeNet.

Dataset

This dataset was created by extend02

Contents

We present a dataset of 3D CAD models (.stl) from the field of mechanical engineering. There are 7 core classes (cover, flange, housing, mounting, rodprobe, sensor, tube) and 5 additional classes (cableconnector, dismiss, diverse, fork, funnelantenna). The dataset has been hand labelled with categories.These models are for demonstration purpose only and do not reflect actual products

ShapeNetCore is a subset of the full ShapeNet dataset with single clean 3D models and manually verified category and alignment annotations. It covers 55 common object categories with about 51,300 unique 3D models. The 12 object categories of PASCAL 3D+, a popular computer vision 3D benchmark dataset, are all covered by ShapeNetCore.

This repository contains ShapeSplats, a large dataset of Gaussian splats spanning 65K objects in 87 unique categories (gathered from ShapeNetCore, ShapeNet-Part, and ModelNet). ShapeSplatsV1 consists of the 52K objects across 55 categories of ShapeNetCore. The data is distributed as ply files where information about each Gaussian is encoded in custom vertex attributes. Please see DATA.md for details about the data. If you use the ShapeSplatsV1 data, you agree to abide by the ShapeNet terms of… See the full description on the dataset page: https://huggingface.co/datasets/ShapeNet/ShapeSplatsV1.

View Shape/net shape china auto parts ltd import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

Sample of the 3D shape matching benchmark used in Do large language vision models understand 3D shapes?

GITHUB project path

Benchmark for testing the ability of Vision Language Models (LVM) to recognize and match 3D objects of the exact same 3D shapes but with different orientation/materials/textures/ enviromnts and light conditions.

Files:

Test_Images.zip:

Sample of the test images used for the benchmark: Each folder contains images used for object/shape matching tests.
The folder is structured: “class_dir/object_dir/image.jpg”
The images in the leaf/edge subfolder all belong to the exact same 3D object instance but with different orientation/materials/environment.
In addition to the jpg image, a png image with the same name is supplied that gives the mask of the object in the jpg image.

Example_Tests.zip:

Example for full image tests, Each of the subfolders contain an example test composed of a 4 panels image marked A-D. Where one (and only one) of the Panel B-C contains an object identical in 3D shape to the object in panel A, but with some difference in orientation, texture, or background and illumination. The test is to guess which panel.

The name of the image contains the correct answer. For each jpg image there is an additional .txt file that contains the query and the response of GPT 4o when given this question. This is a sample for the tests given in the paper.

Benchmark_Generation_Scripts.zip:

Code used to generate the benchmark.

The images supplied here are small samples of the benchmark. To generate the full benchmark use the code. For updated code see this repo.

Evaluation_Scripts.zip

There is a bug in the supplied evaluation script see this url for updated version

Even More test images:

google drive, icedrive ,pcloud

Context

The aim of this dataset is to provide a simple way to get started with 3D computer vision problems such as 3D shape recognition.

Accurate 3D point clouds can (easily and cheaply) be adquired nowdays from different sources:

• RGB-D devices: Google Tango, Microsoft Kinect, etc.
• Lidar.
• 3D reconstruction from multiple images.

However there is a lack of large 3D datasets (you can find a good one here based on triangular meshes); it's especially hard to find datasets based on point clouds (wich is the raw output from every 3D sensing device).

This dataset contains 3D point clouds generated from the original images of the MNIST dataset to bring a familiar introduction to 3D to people used to work with 2D datasets (images).

In the 3D_from_2D notebook you can find the code used to generate the dataset.

You can use the code in the notebook to generate a bigger 3D dataset from the original.

Content

full_dataset_vectors.h5

The entire dataset stored as 4096-D vectors obtained from the voxelization (x:16, y:16, z:16) of all the 3D point clouds.

In adition to the original point clouds, it contains randomly rotated copies with noise.

The full dataset is splitted into arrays:

• X_train (10000, 4096)
• y_train (10000)
• X_test(2000, 4096)
• y_test (2000)

Example python code reading the full dataset:

 with h5py.File("../input/train_point_clouds.h5", "r") as hf:  
   X_train = hf["X_train"][:]
   y_train = hf["y_train"][:]  
   X_test = hf["X_test"][:] 
   y_test = hf["y_test"][:] 

train_point_clouds.h5 & test_point_clouds.h5

5000 (train), and 1000 (test) 3D point clouds stored in HDF5 file format. The point clouds have zero mean and a maximum dimension range of 1.

Each file is divided into HDF5 groups

Each group is named as its corresponding array index in the original mnist dataset and it contains:

• "points" dataset: x, y, z coordinates of each 3D point in the point cloud.
• "normals" dataset: nx, ny, nz components of the unit normal associate to each point.
• "img" dataset: the original mnist image.
• "label" attribute: the original mnist label.

Example python code reading 2 digits and storing some of the group content in tuples:

with h5py.File("../input/train_point_clouds.h5", "r") as hf:  
  a = hf["0"]
  b = hf["1"]  
  digit_a = (a["img"][:], a["points"][:], a.attrs["label"]) 
  digit_b = (b["img"][:], b["points"][:], b.attrs["label"]) 

voxelgrid.py

Simple Python class that generates a grid of voxels from the 3D point cloud. Check kernel for use.

plot3D.py

Module with functions to plot point clouds and voxelgrid inside jupyter notebook. You have to run this locally due to Kaggle's notebook lack of support to rendering Iframes. See github issue here

Functions included:

• array_to_color Converts 1D array to rgb values use as kwarg color in plot_points()

• plot_points(xyz, colors=None, size=0.1, axis=False)

• plot_voxelgrid(v_grid, cmap="Oranges", axis=False)

Acknowledgements

• Website of the original MNIST dataset
• Website of the 3D MNIST dataset

Have fun!

The primary objective of this project is to develop a three-blade MHK rotor with low manufacturing and maintenance costs. The proposed program will design, fabricate and test a novel half-scale low cost, net shape fabricated single piece three-blade MHK rotor with integrated health management technology to demonstrate significant Capital Expenditures (CAPEX) and Operational Expenditures (OPEX) cost reductions due to the novel design and manufacturing process. The proposed project is divided into three major tasks: Task 1: Single Piece Three-blade Kinetic Hydropower System (KHPS) Rotor Full-Scale and Half-Scale Design; Task 2: Composite Manufacturing Trials and Half-Scale Prototype Rotor Fabrication; and Task 3: Material Characterization and Half-Scale Prototype Test and Evaluation. These three tasks include design and analysis of full-scale and half-scale three-blade rotor prototypes using computational fluid dynamics (CFD) and finite-element analysis (FEA), demonstration of a novel half-scale net shape fabrication process, determination of a fatigue threshold composite strain allowable, three-blade rotor mold design, manufacture of half-scale rotor clam shell mold, three-blade rotor test fixture design and fabrication, development of final manufacturing and test plans, manufacture of the half-scale net shape composite single blade and three-blade prototypes, and test and evaluation of the half-scale rotor.

This dataset was generated for the paper: "Adversarial examples within the training distribution: A widespread challenge" using our custom computer graphics pipeline. The paper can be accessed here: https://arxiv.org/abs/2106.16198 and the code used to generate this dataset can be found here: https://github.com/Spandan-Madan/in_distribution_adversarial_examples

Dataset

This dataset was created by sanvik74

Contents