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
SO-Net: Self-Organizing Network for Point Cloud Analysis
This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
We propose a spherical kernel for efficient graph convolution of 3D point clouds.
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
mutianxu/GDANet
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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.
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
OATML/Non-Parametric-Transformers
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NeurIPS 2021
We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input.
Masked Autoencoders for Point Cloud Self-supervised Learning
Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tokens operation, learns high-level latent features from unmasked point patches, aiming to reconstruct the masked point patches.
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
fxia22/kdnet.pytorch
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ICCV 2017
We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds.
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.
3D Point Capsule Networks
yongheng1991/3D-point-capsule-networks
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CVPR 2019
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Point Transformer
engelnico/point-transformer
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In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets.
