3D Object Detection

571 papers with code • 55 benchmarks • 48 datasets

3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.

( Image credit: AVOD )

Libraries

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

Most implemented papers

Joint 3D Proposal Generation and Object Detection from View Aggregation

kujason/avod 6 Dec 2017

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.

Stereo R-CNN based 3D Object Detection for Autonomous Driving

HKUST-Aerial-Robotics/Stereo-RCNN CVPR 2019

Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images.

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

Eisbaer8/DeepGTAV 1 May 2019

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception.

Point-Voxel CNN for Efficient 3D Deep Learning

mit-han-lab/pvcnn NeurIPS 2019

The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution.

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

garrickbrazil/M3D-RPN ICCV 2019

Understanding the world in 3D is a critical component of urban autonomous driving.

PointPainting: Sequential Fusion for 3D Object Detection

Song-Jingyu/PointPainting CVPR 2020

Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature.

Generative Sparse Detection Networks for 3D Single-shot Object Detection

jgwak/GSDN ECCV 2020

3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality.

Group-Free 3D Object Detection via Transformers

zeliu98/Group-Free-3D ICCV 2021

Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training.

Fully Sparse 3D Object Detection

tusimple/sst 20 Jul 2022

To enable efficient long-range LiDAR-based object detection, we build a fully sparse 3D object detector (FSD).

Multi-View 3D Object Detection Network for Autonomous Driving

bostondiditeam/MV3D CVPR 2017

We encode the sparse 3D point cloud with a compact multi-view representation.