3D Object Detection

277 papers with code • 38 benchmarks • 26 datasets

2D object detection classifies the object category and estimates oriented 2D bounding boxes of physical objects from 3D sensor data.

( Image credit: AVOD )


Use these libraries to find 3D Object Detection models and implementations

Most implemented papers

Frustum PointNets for 3D Object Detection from RGB-D Data

charlesq34/frustum-pointnets CVPR 2018

In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

qianguih/voxelnet CVPR 2018

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.

nuScenes: A multimodal dataset for autonomous driving

nutonomy/nuscenes-devkit CVPR 2020

Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.

Deep Hough Voting for 3D Object Detection in Point Clouds

facebookresearch/votenet ICCV 2019

Current 3D object detection methods are heavily influenced by 2D detectors.

3D Bounding Box Estimation Using Deep Learning and Geometry

smallcorgi/3D-Deepbox CVPR 2017

In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box.

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

maudzung/Complex-YOLOv4-Pytorch 16 Mar 2018

We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

sshaoshuai/PointRCNN CVPR 2019

In this paper, we propose PointRCNN for 3D object detection from raw point cloud.

Center-based 3D Object Detection and Tracking

tianweiy/CenterPoint CVPR 2021

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.

PointPillars: Fast Encoders for Object Detection from Point Clouds

nutonomy/second.pytorch CVPR 2019

These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

frgfm/Holocron ICLR 2022

Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems.