3D Object Recognition
27 papers with code • 4 benchmarks • 8 datasets
3D object recognition is the task of recognising objects from 3D data.
Note that there are related tasks you can look at, such as 3D Object Detection which have more leaderboards.
(Image credit: Look Further to Recognize Better)
Benchmarks
These leaderboards are used to track progress in 3D Object Recognition
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Datasets
ModelNet
SHREC
Washington RGB-D
Cube++
DOORS
CY101 Dataset
Corn Seeds Dataset
Latest papers
One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation
memoatwit/universalperturbation
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This paper presents a novel universal perturbation method for generating robust multi-view adversarial examples in 3D object recognition.
Lifting Multi-View Detection and Tracking to the Bird's Eye View
Taking advantage of multi-view aggregation presents a promising solution to tackle challenges such as occlusion and missed detection in multi-object tracking and detection.
R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition
shanshuo/MVT
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Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community.
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models
Robots operating in human-centered environments, such as retail stores, restaurants, and households, are often required to distinguish between similar objects in different contexts with a high degree of accuracy.
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
facebookresearch/omni3d
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CVPR 2023
In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e. g. urban driving scenes.
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
MVT: Multi-view Vision Transformer for 3D Object Recognition
shanshuo/MVT
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Nevertheless, multi-view CNN models cannot model the communications between patches from different views, limiting its effectiveness in 3D object recognition.
Explainability-Aware One Point Attack for Point Cloud Neural Networks
explain3d/exp-one-point-atk-pc
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With the proposition of neural networks for point clouds, deep learning has started to shine in the field of 3D object recognition while researchers have shown an increased interest to investigate the reliability of point cloud networks by adversarial attacks.
Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks
In this paper, we proposed a hybrid model architecture consists of a dynamically growing dual-memory recurrent neural network (GDM) and an autoencoder to tackle object recognition and grasping simultaneously.
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable Networks
Towards addressing this challenge, we propose a new deep transfer learning approach based on a dynamic architectural method to make robots capable of open-ended learning about new 3D object categories.
