3D Object Classification
43 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
Classification of Single-View Object Point Clouds
By adapting existing ModelNet40 and ScanNet datasets to the single-view, partial setting, experiment results can verify the necessity of object pose estimation and superiority of our PAPNet to existing classifiers.
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
Moreover, the performance of advanced approaches degrades dramatically for past learned classes (i. e., catastrophic forgetting), due to the irregular and redundant geometric structures of 3D point cloud data.
Generalized Multi-view Shared Subspace Learning using View Bootstrapping
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks.
L3DOC: Lifelong 3D Object Classification
To further transfer the task-specific knowledge from previous tasks to the new coming classification task, a memory attention mechanism is proposed to connect the current task with relevant previously tasks, which can effectively prevent catastrophic forgetting via soft-transferring previous knowledge.
Data-Free Point Cloud Network for 3D Face Recognition
To ease the inconsistent distribution between model data and real faces, different point sampling methods are used in train and test phase.
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text
We illustrate the effectiveness of using multi-task learning with a multi-channel convolutional neural network as the shared representation and use additional inputs identified by researchers as indicatives in detecting mental disorders to enhance the model predictability.
Addressing the Sim2Real Gap in Robotic 3D Object Classification
In this work, we examine this gap in a robotic context by specifically addressing the problem of classification when transferring from artificial CAD models to real reconstructed objects.
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition
We present a generic, flexible and 3D rotation invariant framework based on spherical symmetry for point cloud recognition.
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis
Current neural networks for 3D object recognition are vulnerable to 3D rotation.
Octree guided CNN with Spherical Kernels for 3D Point Clouds
We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds.
