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.

SeaLab/ShapeNet dataset hosted on Hugging Face and contributed by the HF Datasets community

A dataset for 3D shape generation, using the five shape categories selected from ShapeNet Core V1

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.

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.

The ModelNet40 zero-shot 3D classification performance of models pretrained on ShapeNet only.

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

Dataset

This dataset was created by Jeremy26

Contents

The dataset used in the paper is ShapeNet, a large-scale 3D shape dataset, and ModelNet40, a dataset for 3D object classification.

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.

A large-scale 3D model repository containing over 16,000 3D models.

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).

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.

The convolutional neural networks (CNNs) are a powerful tool of image classification that has been widely adopted in applications of automated scene segmentation and identification. However, the mechanisms underlying CNN image classification remain to be elucidated. In this study, we developed a new approach to address this issue by investigating transfer of learning in representative CNNs (AlexNet, VGG, ResNet-101, and Inception-ResNet-v2) on classifying geometric shapes based on local/global features or invariants. While the local features are based on simple components, such as orientation of line segment or whether two lines are parallel, the global features are based on the whole object such as whether an object has a hole or whether an object is inside of another object. Six experiments were conducted to test two hypotheses on CNN shape classification. The first hypothesis is that transfer of learning based on local features is higher than transfer of learning based on global features. The second hypothesis is that the CNNs with more layers and advanced architectures have higher transfer of learning based global features. The first two experiments examined how the CNNs transferred learning of discriminating local features (square, rectangle, trapezoid, and parallelogram). The other four experiments examined how the CNNs transferred learning of discriminating global features (presence of a hole, connectivity, and inside/outside relationship). While the CNNs exhibited robust learning on classifying shapes, transfer of learning varied from task to task, and model to model. The results rejected both hypotheses. First, some CNNs exhibited lower transfer of learning based on local features than that based on global features. Second the advanced CNNs exhibited lower transfer of learning on global features than that of the earlier models. Among the tested geometric features, we found that learning of discriminating inside/outside relationship was the most difficult to be transferred, indicating an effective benchmark to develop future CNNs. In contrast to the “ImageNet” approach that employs natural images to train and analyze the CNNs, the results show proof of concept for the “ShapeNet” approach that employs well-defined geometric shapes to elucidate the strengths and limitations of the computation in CNN image classification. This “ShapeNet” approach will also provide insights into understanding visual information processing the primate visual systems.

Dataset

This dataset was created by guxue17

Contents

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

Comparison of mean 3D IoU score with the baseline reconstruction methods on two categories of ShapeNet datasets.

Credit report of Shape Net Shape China contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The ShapeNet dataset is a large-scale benchmark for 3D shape analysis and generation.