computer-vision-5

3D Point Cloud Classification

3260 papers • 126 benchmarks • 313 datasets

Image: Qi et al

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-point-cloud-classification-5

Trend

Dataset

Best Model

Actions

ModelNet40

ScanObjectNN

IntrA

Libraries

i

Use these libraries to find 3d-point-cloud-classification-5 models and implementations

vinits5/learning3d

3 papers 641

Datasets

Subtasks

3D Object Classification Few-Shot 3D Point Cloud Classification Zero-Shot Transfer 3D Point Cloud Classification

Most implemented papers

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

C. Qi, Hao Su, L. Guibas, L. Yi•Tue Jun 06 2017

A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly.

12754

Content

ModelNet40-C

Sydney Urban Objects

jiachens/ModelNet40-C

3 papers 199

3 papers 89

2 papers 1,646

2 papers 1,349

2 papers 1,079

rusty1s/pytorch_cluster

2 papers 753

lmd0311/pointmamba

2 papers 209

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Paper Graph

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

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Dynamic Graph CNN for Learning on Point Clouds

M. Bronstein, Ziwei Liu, Yue Wang, Yongbin Sun, S. Sarma, J. Solomon•Tue Jan 23 2018

This work proposes a new neural network module suitable for CNN-based high-level tasks on point clouds, including classification and segmentation called EdgeConv, which acts on graphs dynamically computed in each layer of the network.

6827 0

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Perceiver: General Perception with Iterative Attention

O. Vinyals, Andrew Zisserman, Felix Gimeno, João Carreira, Andrew Jaegle, Andrew Brock•Wed Mar 03 2021

This paper introduces the Perceiver - a model that builds upon Transformers and hence makes few architectural assumptions about the relationship between its inputs, but that also scales to hundreds of thousands of inputs, like ConvNets.

1285 0

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PCT: Point cloud transformer

Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, Ralph Robert Martin, Shimin Hu, Junxiong Cai•Wed Dec 16 2020

A novel framework based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing, is presented, which is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.

1973 0

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PointConv: Deep Convolutional Networks on 3D Point Clouds

Zhongang Qi, Wenxuan Wu, Fuxin Li•Fri Nov 16 2018

The dynamic filter is extended to a new convolution operation, named PointConv, which can be applied on point clouds to build deep convolutional networks and is able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds.

1784 0

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KPConv: Flexible and Deformable Convolution for Point Clouds

C. Qi, L. Guibas, Jean-Emmanuel Deschaud, F. Goulette, Hugues Thomas, B. Marcotegui•Wed Apr 17 2019

KPConv is a new design of point convolution, i.e. that operates on point clouds without any intermediate representation, that outperform state-of-the-art classification and segmentation approaches on several datasets.

3040 0

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Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

B. Kailkhura, Chaowei Xiao, Zhiding Yu, Jiachen Sun, Qingzhao Zhang, Z. Mao•Thu Jan 27 2022

This paper presents ModelNet40-C, the first comprehensive benchmark on 3D point cloud corruption robustness, consisting of 15 common and realistic corruptions, and unveils that Transformer-based architectures with proper training recipes achieve the strongest robustness.

111 0

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Relation-Shape Convolutional Neural Network for Point Cloud Analysis

Yongcheng Liu, Bin Fan, Shiming Xiang, Chunhong Pan•Mon Apr 15 2019

RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration for point cloud analysis, achieves the state of the arts and a hierarchical architecture can be developed to achieve contextual shape-aware learning for Point cloud analysis.

993 0

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