About

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

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Datasets

Greatest papers with code

Sparse 3D convolutional neural networks

12 May 2015facebookresearch/SparseConvNet

We have implemented a convolutional neural network designed for processing sparse three-dimensional input data.

3D OBJECT RECOGNITION TEMPORAL ACTION LOCALIZATION

BlenderProc

25 Oct 2019DLR-RM/BlenderProc

BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.

3D OBJECT RECOGNITION DEPTH IMAGE ESTIMATION POSE ESTIMATION SEMANTIC SEGMENTATION SURFACE NORMALS ESTIMATION

SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation

ICCV 2019 yzhou359/MakeItTalk

In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings.

3D OBJECT RECOGNITION SCENE GENERATION

Volumetric and Multi-View CNNs for Object Classification on 3D Data

CVPR 2016 charlesq34/3dcnn.torch

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.

3D OBJECT RECOGNITION 3D POINT CLOUD CLASSIFICATION OBJECT CLASSIFICATION

FPNN: Field Probing Neural Networks for 3D Data

NeurIPS 2016 yangyanli/FPNN

Each field probing filter is a set of probing points --- sensors that perceive the space.

3D OBJECT RECOGNITION

Task-Aware Monocular Depth Estimation for 3D Object Detection

17 Sep 2019WXinlong/ForeSeE

In this paper, we first analyse the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground depth and background depth using separate optimization objectives and depth decoders.

3D OBJECT DETECTION 3D OBJECT RECOGNITION MONOCULAR DEPTH ESTIMATION

Learning a Hierarchical Latent-Variable Model of 3D Shapes

17 May 2017lorenmt/vsl

We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.

3D OBJECT CLASSIFICATION 3D OBJECT RECOGNITION 3D RECONSTRUCTION 3D SHAPE GENERATION

Triplet-Center Loss for Multi-View 3D Object Retrieval

CVPR 2018 popcornell/keras-triplet-center-loss

Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected.

3D OBJECT RECOGNITION 3D OBJECT RETRIEVAL 3D SHAPE CLASSIFICATION METRIC LEARNING

Multi-level 3D CNN for Learning Multi-scale Spatial Features

30 May 2018idealab-isu/GPView

The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object.

3D OBJECT RECOGNITION