Supervised Only 3D Point Cloud Classification
14 papers with code • 1 benchmarks • 1 datasets
Libraries
Use these libraries to find Supervised Only 3D Point Cloud Classification models and implementationsMost implemented papers
Attention Is All You Need
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
Point cloud is an important type of geometric data structure.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
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
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 Mamba to process 3-D point cloud data more effectively, we propose a novel Consistent Traverse Serialization method 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
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
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.