3D Point Cloud Linear Classification
15 papers with code • 2 benchmarks • 2 datasets
Training a linear classifier(e.g. SVM) on the embeddings/representations of 3D point clouds. The embeddings/representations are usually trained in an unsupervised manner.
Benchmarks
These leaderboards are used to track progress in 3D Point Cloud Linear Classification
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Libraries
Use these libraries to find 3D Point Cloud Linear Classification models and implementations
ModelNet
ScanObjectNN
Most implemented papers
Unsupervised Point Cloud Pre-Training via Occlusion Completion
We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc.
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
The most popular and accessible 3D representation, i. e., point clouds, involves discrete samples of the underlying continuous 3D surface.
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
mohamedafham/crosspoint
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CVPR 2022
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
snehaputul/CrossMoCo
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20th Conference on Robots and Vision (CRV) 2023
In this study, we introduce a novel selfsupervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2D rendered images of the point clouds.
