3D Part Segmentation
65 papers with code • 2 benchmarks • 6 datasets
Segmenting 3D object parts
( Image credit: MeshCNN: A Network with an Edge )
Libraries
Use these libraries to find 3D Part Segmentation models and implementations
ShapeNet
IntrA
LiDAR-MOS
Teeth3DS
Workshop Tools Dataset
TomoSAM
Most implemented papers
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
tiangexiang/CurveNet
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ICCV 2021
Discrete point cloud objects lack sufficient shape descriptors of 3D geometries.
PVT: Point-Voxel Transformer for Point Cloud Learning
HaochengWan/PVT
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The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks.
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation
edshkim98/GAGCN
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British Machine Vision Conference (BMVC) 2021
To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve segmentation accuracy and b) a graph convolution, GeoEdgeConv, as a means of local feature aggregation to improve segmentation accuracy and space and time complexities.
Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding
To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
yangyangyang127/pointclip_v2
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ICCV 2023
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Generalizable 3D part segmentation is important but challenging in vision and robotics.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
facebookresearch/SparseConvNet
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We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results.
Point Convolutional Neural Networks by Extension Operators
matanatz/pcnn
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This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds.
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
xyf513/SpiderCNN
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ECCV 2018
Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds.
