3D Object Classification
48 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.
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.
PointMixer: MLP-Mixer for Point Cloud Understanding
lifebeyondexpectations/eccv22-pointmixer
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MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer.
PointLLM: Empowering Large Language Models to Understand Point Clouds
The unprecedented advancements in Large Language Models (LLMs) have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding.
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.
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.
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks.
Uni3D: Exploring Unified 3D Representation at Scale
baaivision/uni3d
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Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.
