3D Object Classification
42 papers with code • 3 benchmarks • 6 datasets
3D Object Classification is the task of predicting the class of a 3D object point cloud. It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric.
Image: Sedaghat et al
Benchmarks
These leaderboards are used to track progress in 3D Object Classification
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Datasets
ModelNet
3RScan
Cyclone Data
UR5 Tool Dataset
Latest papers with no code
Improving Normalization with the James-Stein Estimator
In this paper, first, we establish that normalization layers in deep learning use inadmissible estimators for mean and variance.
Fast Sparse 3D Convolution Network with VDB
We proposed a new Convolution Neural Network implementation optimized for sparse 3D data inference.
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease
However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions.
MVImgNet: A Large-scale Dataset of Multi-view Images
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity
We present an unsupervised deep learning model for 3D object classification.
HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces
Next, as every two neighbor edges compose a surface, we obtain the edge-level representation of each anchor edge via surface-to-edge aggregation over all neighbor surfaces.
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding
To solve it, we proposed PointCMC, a novel cross-modal method to model multi-scale correspondences across modalities for self-supervised point cloud representation learning.
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives
Further study suggests that our strategy can improve the model performance by pretraining and fine-tuning scheme, especially for the dataset with a small scale.
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting
This paper proposes a general unsupervised approach, named \textbf{ConClu}, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting.
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation
As the core module of the DPFA-Net, we propose a Feature Aggregation layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism.
