Supervised Only 3D Point Cloud Classification
14 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Supervised Only 3D Point Cloud Classification
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Libraries
Use these libraries to find Supervised Only 3D Point Cloud Classification models and implementations
ScanObjectNN
Most implemented papers
Attention Is All You Need
tensorflow/tensor2tensor
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NeurIPS 2017
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
charlesq34/pointnet
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CVPR 2017
Point cloud is an important type of geometric data structure.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
yanx27/Pointnet_Pointnet2_pytorch
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NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Dynamic Graph CNN for Learning on Point Clouds
WangYueFt/dgcnn
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Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions.
Point Cloud Mamba: Point Cloud Learning via State Space Model
To enable more effective processing of 3-D point cloud data by Mamba, we propose a novel Consistent Traverse Serialization to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent.
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
ma-xu/pointmlp-pytorch
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ICLR 2022
We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively.
Surface Representation for Point Clouds
hancyran/RepSurf
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CVPR 2022
Based on a simple baseline of PointNet++ (SSG version), Umbrella RepSurf surpasses the previous state-of-the-art by a large margin for classification, segmentation and detection on various benchmarks in terms of performance and efficiency.
Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space
To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short.
