3D Point Cloud Classification
127 papers with code • 5 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in 3D Point Cloud Classification
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Libraries
Use these libraries to find 3D Point Cloud Classification models and implementations
ModelNet
ScanObjectNN
IntrA
ModelNet40-C
Sydney Urban Objects
Teeth3DS
Subtasks
Most implemented papers
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
zyh16143998882/iccv23-idpt
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ICCV 2023
To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models.
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
qizekun/ShapeLLM
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This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
Volumetric and Multi-View CNNs for Object Classification on 3D Data
charlesq34/3dcnn.torch
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CVPR 2016
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
When working with three-dimensional data, choice of representation is key.
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.
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
mys007/ecc
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CVPR 2017
A number of problems can be formulated as prediction on graph-structured data.
Adversarial shape perturbations on 3D point clouds
Daniel-Liu-c0deb0t/Adversarial-point-perturbations-on-3D-objects
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The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving.
Geometric Back-projection Network for Point Cloud Classification
ShiQiu0419/GBNet
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As the basic task of point cloud analysis, classification is fundamental but always challenging.
SampleNet: Differentiable Point Cloud Sampling
itailang/SampleNet
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CVPR 2020
As the size of the point cloud grows, so do the computational demands of these tasks.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
