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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 #42 on 3D Object Detection on nuScenes
Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving.
3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information.
Ranked #17 on Vehicle Pose Estimation on KITTI Cars Hard
Understanding the world in 3D is a critical component of urban autonomous driving.
Ranked #15 on Vehicle Pose Estimation on KITTI Cars Hard
We further verify the power of the proposed module with a neural network designed for monocular depth prediction.
Ranked #1 on Monocular 3D Object Detection on KITTI Cars Moderate
This allows us to reason holistically about the spatial configuration of the scene in a domain where scale is consistent and distances between objects are meaningful.
Following the pipeline of two-stage 3D detection algorithms, we detect 2D object proposals in the input image and extract a point cloud frustum from the pseudo-LiDAR for each proposal.
In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.
Ranked #19 on Vehicle Pose Estimation on KITTI Cars Hard