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We propose a complete pipeline that allows object detection and simultaneously estimate the pose of these multiple object instances using just a single image. A novel keypoint regression scheme with a cross-ratio term is introduced that exploits prior information about the objects shape and size to regress and find specific feature points. Further, a priori 3D information about the object is used to match 2D-3D correspondences and accurately estimate object positions up to a distance of 15m. A detailed discussion of the results and an in-depth analysis of the pipeline is presented. The pipeline runs efficiently on a low-powered Jetson TX2 and is deployed as part of the perception pipeline on a real-time autonomous vehicle cruising at a top speed of 54 km/hr.
In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box p
We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with k
In this work, we present a modified fuzzy decision forest for real-time 3D object pose estimation based on typical template representation. We employ an extra preemptive background rejector node in the decision forest framework to terminate the exami
Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric c
Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learnin