3D Object Classification
43 papers with code • 3 benchmarks • 6 datasets
3D Object Classification is the task of predicting the class of a 3D object point cloud. It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric.
Image: Sedaghat et al
Benchmarks
These leaderboards are used to track progress in 3D Object Classification
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Datasets
ModelNet
3RScan
Cyclone Data
UR5 Tool Dataset
Most implemented papers
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
hkust-vgd/scanobjectnn
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ICCV 2019
From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions.
InSphereNet: a Concise Representation and Classification Method for 3D Object
Unlike previous methods that use points, voxels, or multi-view images as inputs of deep neural network (DNN), the proposed method constructs a class of more representative features named infilling spheres from signed distance field (SDF).
FPConv: Learning Local Flattening for Point Convolution
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis.
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
raoyongming/PointGLR
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CVPR 2020
Based on this hypothesis, we propose to learn point cloud representation by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape without human supervision.
Cascaded Refinement Network for Point Cloud Completion with Self-supervision
xiaogangw/cascaded-point-completion
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This is to mitigate the dependence of existing approaches on large amounts of ground truth training data that are often difficult to obtain in real-world applications.
A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification
CorleoneJW/3D-Retrieval-System
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Voxel-based 3D object classification has been thoroughly studied in recent years.
Regularization Strategy for Point Cloud via Rigidly Mixed Sample
dogyoonlee/RSMix-official
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CVPR 2021
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks.
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
myavartanoo/PolyNet_PyTorch
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3D shape representation and its processing have substantial effects on 3D shape recognition.
diffConv: Analyzing Irregular Point Clouds with an Irregular View
Standard spatial convolutions assume input data with a regular neighborhood structure.
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
