3D Shape Classification
29 papers with code • 1 benchmarks • 1 datasets
Image: Sun et al
Libraries
Use these libraries to find 3D Shape Classification models and implementationsMost implemented papers
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
3D shape representation and its processing have substantial effects on 3D shape recognition.
DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion
We reverse the conventional design of applying convolution on voxels and attention to points.
diffConv: Analyzing Irregular Point Clouds with an Irregular View
Standard spatial convolutions assume input data with a regular neighborhood structure.
Masked Discrimination for Self-Supervised Learning on Point Clouds
Masked autoencoding has achieved great success for self-supervised learning in the image and language domains.
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition
In this paper, we explore the possibility of boosting deep 3D point cloud encoders by transferring visual knowledge extracted from deep 2D image encoders under a standard teacher-student distillation workflow.
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers
Transformer with its underlying attention mechanism and the ability to capture long-range dependencies makes it become a natural choice for unordered point cloud data.
MVTN: Learning Multi-View Transformations for 3D Understanding
Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes.
ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding
For example, the image branch in CrossPoint is $\sim$8. 3x heavier than the point cloud branch leading to higher complexity and latency.
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Specifically, we extend the random features method of Rahimi & Recht 2007 by deriving a version that is invariant to three-dimensional rotations and showing that it is fast to evaluate on point cloud data.