computer-vision-9

3D Part Segmentation

3260 papers • 126 benchmarks • 313 datasets

Segmenting 3D object parts ( Image credit: MeshCNN: A Network with an Edge )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-part-segmentation-9

Trend

Dataset

Best Model

Actions

ShapeNet-Part

IntrA

Libraries

i

Use these libraries to find 3d-part-segmentation-9 models and implementations

isl-org/Open3D-ML

3 papers 1,662

Datasets

ShapeNet

IntrA

LiDAR-MOS

Workshop Tools Dataset

TomoSAM

Subtasks

No subtasks available.

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

vinits5/learning3d

3 papers 646

facebookresearch/SparseConvNet

2 papers 1,990

yangyanli/PointCNN

2 papers 1,350

0

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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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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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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3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks

M. Lindenbaum, Yizhak Ben-Shabat, A. Fischer•Tue Nov 21 2017

A novel 3D point cloud representation called 3D Modified Fisher Vectors (3DmFV) is proposed, which combines the discrete structure of a grid with continuous generalization of Fisher vectors, in a compact and computationally efficient way.

71 0

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