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
Latest papers
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds
trlou/hit-adv
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We find that concealing deformation perturbations in areas insensitive to human eyes can achieve a better trade-off between imperceptibility and adversarial strength, specifically in parts of the object surface that are complex and exhibit drastic curvature changes.
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
qizekun/ReCon
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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.
ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification
Through extensive experiments, we demonstrate that our PointMLS achieves state-of-the-art results on ModelNet-O and competitive results on regular datasets, and it is robust and effective.
Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
zyh16143998882/aaai24-pointfemae
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Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct.
DualMLP: a two-stream fusion model for 3D point cloud classification
snehaputul/DualMLP
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The Visual Computer 2023
The SparseNet, a relatively larger network, samples a small number of points from the complete point cloud, while the DenseNet, a lightweight network, takes in a larger number of points as input.
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
The proposed method decouples functions between the decoder and the encoder by introducing a mask regressor, which predicts the masked patch representation from the visible patch representation encoded by the encoder and the decoder reconstructs the target from the predicted masked patch representation.
Decoupled Local Aggregation for Point Cloud Learning
matrix-asc/dela
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In this work, we propose to decouple the explicit modelling of spatial relations from local aggregation.
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
mr-neko/jm3d
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Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality.
Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification
With the growth of 3D sensing technology, deep learning system for 3D point clouds has become increasingly important, especially in applications like autonomous vehicles where safety is a primary concern.
