3D Shape Classification
29 papers with code • 1 benchmarks • 1 datasets
Image: Sun et al
Benchmarks
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Libraries
Use these libraries to find 3D Shape Classification models and implementations
Pix3D
Latest papers with no code
Ensemble Quadratic Assignment Network for Graph Matching
In this paper, we propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition
We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-trained 2D model.
Multi-View Representation is What You Need for Point-Cloud Pre-Training
Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction.
APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
To tackle these problems, we propose Asymmetric Parallel Point Transformer (APPT).
Frequency-domain Learning for Volumetric-based 3D Data Perception
Frequency-domain learning draws attention due to its superior tradeoff between inference accuracy and input data size.
Robust 3D Shape Classification via Non-Local Graph Attention Network
Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85. 4%) of NLGAT is improved by 39. 4% compared with the best development of other methods.
Rethinking Rotation Invariance with Point Cloud Registration
Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned.
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons
Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to input perturbations.
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds
Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds.
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views
In addition, CoLAV introduces a novel mechanism for the dynamic generation of shape-instance-dependent adversarial views as positive pairs to adversarially train robust contrastive learning models towards the learning of more informative 3D shape representation.
