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computer-vision-7

3D Object Recognition

3260 papers • 126 benchmarks • 313 datasets

3D object recognition is the task of recognising objects from 3D data. Note that there are related tasks you can look at, such as 3D Object Detection which have more leaderboards. (Image credit: Look Further to Recognize Better)

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-object-recognition-7

Trend

Dataset

Best Model

Actions

ModelNet40

ModelNet40

SHREC11, Split16-4

SHREC11, Split16-4

SHREC11, Split10-10

Libraries

i

Use these libraries to find 3d-object-recognition-7 models and implementations

Datasets

ModelNet

SHREC

Washington RGB-D

Washington RGB-D

Cube++

DOORS

CY101 Dataset

Subtasks

No subtasks available.

Most implemented papers

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Jiajun Wu, J. Tenenbaum, Tianfan Xue, Chengkai Zhang, Bill Freeman•Sun Oct 23 2016

A novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets, and a powerful 3D shape descriptor which has wide applications in 3D object recognition.

2085

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

SHREC11, Split10-10

Cube Engraving

Cube Engraving

The RBO Dataset of Articulated Objects and Interactions

The RBO Dataset of Articulated Objects and Interactions

Corn Seeds Dataset

Corn Seeds Dataset

0

Sparse 3D convolutional neural networks

Benjamin Graham•Thu Apr 30 2015

A convolutional neural network designed for processing sparse three-dimensional input data and experiment with CNNs on the 2D triangular-lattice and 3D tetrahedral- lattice is implemented.

219 0

Volumetric and Multi-view CNNs for Object Classification on 3D Data

C. Qi, Hao Su, L. Guibas, Angela Dai, M. Nießner, Mengyuan Yan•Mon Apr 11 2016

This paper introduces two distinct network architectures of volumetric CNNs and examines multi-view CNNs, providing a better understanding of the space of methods available for object classification on 3D data.

1625 0

SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation

E. Kalogerakis, Yang Zhou, Zachary While•Wed Jul 24 2019

A neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings by weighting messages through an attention mechanism, which significantly outperforms state-of-the-art approaches in terms of correctly predicting objects missing in a scene.

103 0

MVT: Multi-view Vision Transformer for 3D Object Recognition

Shuo Chen, Tan Yu, Ping Li•Sun Oct 24 2021

This work proposes a Multi-view Vision Transformer (MVT) for 3D object recognition, and develops a global-local structure for the MVT that takes much less inductive bias compared with its CNN counterparts.

58 0

R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition

Shuo Chen, Tan Yu, Ping Li•Sat Nov 19 2022

This work presents an MLP-based architecture termed as Round-Roll MLP, which extends the spatial-shift MLP backbone by considering the communications between patches from different views and achieves competitive performance compared with existing state-of-the-art methods.

4 0

FPNN: Field Probing Neural Networks for 3D Data

C. Qi, Hao Su, L. Guibas, Yangyan Li, Sören Pirk•Thu May 19 2016

This work represents 3D spaces as volumetric fields, and proposes a novel design that employs field probing filters to efficiently extract features from them, showing that field probing is significantly more efficient than 3DCNNs, while providing state-of-the-art performance, on classification tasks for 3D object recognition benchmark datasets.

301 0

Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes

Sambit Ghadai, Aditya Balu, A. Krishnamurthy, S. Sarkar•Sun Nov 12 2017

A unique 3D-CNN based Gradient-weighted Class Activation Mapping method (3D-GradCAM) for visual explanations of the distinct local geometric features of interest within an object to enable efficient learning of 3D geometries.

7 0

Learning a Hierarchical Latent-Variable Model of 3D Shapes

C. Lee Giles, Shikun Liu, Alexander Ororbia•Tue May 16 2017

We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion. Through the use of skip-connections, our model can successfully learn and infer a latent, hierarchical representation of objects. Furthermore, realistic 3D objects can be easily generated by sampling the VSL's latent probabilistic manifold. We show that our generative model can be trained end-to-end from 2D images to perform single image 3D model retrieval. Experiments show, both quantitatively and qualitatively, the improved generalization of our proposed model over a range of tasks, performing better or comparable to various state-of-the-art alternatives.

67 0

Adding a benchmark result helps the community track progress.

3D Object Recognition | State-of-the-Art