3D Object Recognition
24 papers with code • 4 benchmarks • 7 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)
These leaderboards are used to track progress in 3D Object Recognition
Most implemented papers
BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
We study the problem of 3D object generation.
Sparse 3D convolutional neural networks
We have implemented a convolutional neural network designed for processing sparse three-dimensional input data.
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings.
MVT: Multi-view Vision Transformer for 3D Object Recognition
Nevertheless, multi-view CNN models cannot model the communications between patches from different views, limiting its effectiveness in 3D object recognition.
R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition
Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community.
FPNN: Field Probing Neural Networks for 3D Data
Each field probing filter is a set of probing points --- sensors that perceive the space.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.