3D Point Cloud Linear Classification
13 papers with code • 1 benchmarks • 1 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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ModelNet
Most implemented papers
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
zck119/3dgan-release
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NeurIPS 2016
We study the problem of 3D object generation.
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
AnTao97/UnsupervisedPointCloudReconstruction
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CVPR 2018
Recent deep networks that directly handle points in a point set, e. g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation.
SO-Net: Self-Organizing Network for Point Cloud Analysis
This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
By fine-tuning on downstream tasks, Point-M2AE achieves 86. 43% accuracy on ScanObjectNN, +3. 36% to the second-best, and largely benefits the few-shot classification, part segmentation and 3D object detection with the hierarchical pre-training scheme.
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
zrrskywalker/i2p-mae
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CVPR 2023
Pre-training by numerous image data has become de-facto for robust 2D representations.
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
qizekun/ReCon
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This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
Self-supervised Learning of Point Clouds via Orientation Estimation
OmidPoursaeed/Self_supervised_Learning_Point_Clouds
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A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision.
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Microsoft/O-CNN
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Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods.
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.
