155 papers with code • 32 benchmarks • 16 datasets
2D object detection classifies the object category and estimates oriented 2D bounding boxes of physical objects from 3D sensor data.
( Image credit: AVOD )
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.
Ranked #1 on Object Localization on KITTI Cars Easy
Second, we propose a more advanced framework, PV-RCNN-v2, for more efficient and accurate 3D detection.
Ranked #1 on 3D Object Detection on KITTI Cars Hard val
In this technical report, we present the top-performing LiDAR-only solutions for 3D detection, 3D tracking and domain adaptation three tracks in Waymo Open Dataset Challenges 2020.
We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval.
Ranked #1 on Monocular 3D Object Detection on Google Objectron
In this technical report, we study this problem with a practice built on fully convolutional single-stage detector and propose a general framework FCOS3D.
Ranked #50 on 3D Object Detection on nuScenes
Due to the fact that multi-modality data augmentation must maintain consistency between point cloud and images, recent methods in this field typically use relatively insufficient data augmentation.