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computer-vision-5

3D Object Classification

3260 papers • 126 benchmarks • 313 datasets

3D Object Classification is the task of predicting the class of a 3D object point cloud. It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric. Image: Sedaghat et al

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-object-classification-21

Trend

Dataset

Best Model

Actions

ModelNet40

ModelNet40

ModelNet10

ModelNet10

3R-Scan

3R-Scan

Libraries

i

Use these libraries to find 3d-object-classification-21 models and implementations

Datasets

ModelNet

3RScan

Cyclone Data

EUEN17037_Daylight_and_View_Standard_TestDataSet

EUEN17037_Daylight_and_View_Standard_TestDataSet

UR5 Tool Dataset

UR5 Tool Dataset

CY101 Dataset

Subtasks

Cube Engraving Classification

Most implemented papers

Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

Naveed Akhtar, A. Mian, Huan Lei•Thu Sep 19 2019

The proposed spherical kernel for efficient graph convolution of 3D point clouds maintains translation-invariance and asymmetry properties, where the former guarantees weight sharing among similar local structures in the data and the latter facilitates fine geometric learning.

180

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

0

Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud

Y. Qiao, Xiaojuan Qi, Mutian Xu, Junhao Zhang, Zhipeng Zhou, Mingye Xu•Sat Dec 19 2020

GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components and exploits Sharp-Gentle Complementary Attention Module that regards the features from sharp and Gentle variation components as two holistic representations.

168 0

PointLLM: Empowering Large Language Models to Understand Point Clouds

Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, Dahua Lin•Wed Aug 30 2023

Experimental results reveal PointLLM's superior performance over existing 2D and 3D baselines, with a notable achievement in human-evaluated object captioning tasks where it surpasses human annotators in over 50% of the samples.

293 0

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

N. Komodakis, M. Simonovsky•Sun Apr 09 2017

This work generalizes the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying size and connectivity.

1317 0

Point Transformer

Nico Engel, Vasileios Belagiannis, K. Dietmayer•Sun Nov 01 2020

This work proposes SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score, to extract local and global features and relate both representations by introducing the local-global attention mechanism.

2509 0

3D Point Capsule Networks

Federico Tombari, Yongheng Zhao, Tolga Birdal, Haowen Deng•Wed Dec 26 2018

3D capsule networks are proposed, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data and enables new applications such as part interpolation and replacement.

358 0

SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

N. Navab, Federico Tombari, Johanna Wald, Shun-cheng Wu, Keisuke Tateno•Fri Mar 26 2021

This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames by means of a graph neural network and proposes a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario.

202 0

PointMixer: MLP-Mixer for Point Cloud Understanding

François Rameau, In-So Kweon, Jaesik Park, Jaesung Choe, Chunghyun Park•Sun Nov 21 2021

This paper proposes PointMixer, a universal point set operator that facilitates information sharing among unstructured 3D points by simply replacing token-mixing MLPs with a softmax function, which can be broadly used in the network as inter-set mixing, intra- set mixing, and pyramid mixing.

118 0

Uni3D: Exploring Unified 3D Representation at Scale

Junsheng Zhou, Jinsheng Wang, Baorui Ma, Yu-Shen Liu, Tiejun Huang, Xinlong Wang•Mon Oct 09 2023

Uni3D, a 3D foundation model to explore the unified 3D representation at scale, efficiently scale up Uni3D to one billion parameters, and set new records on a broad range of 3D tasks, such as zero-shot classification, few- shot classification, open-world understanding and part segmentation.

174 0

Open-Pose 3D zero-shot learning: Benchmark and challenges

Weiguang Zhao, Guanyu Yang, Rui Zhang, Chenru Jiang, Chaolong Yang, Yuyao Yan, Amir Hussain, Kaizhu Huang•Mon Dec 11 2023

A more realistic and challenging scenario named open-pose 3D zero-shot classification, focusing on the recognition of 3D objects regardless of their orientation, is proposed, to make validation more compelling and not just limited to existing CLIP-based methods.

9 0

Adding a benchmark result helps the community track progress.

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