Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road

Understand

This paper introduces an approach to produce accurate 3D detection boxes for objects on the ground using single monocular images.

• We do so by merging 2D visual cues, 3D object dimensions, and ground plane constraints to produce boxes that are robust against small errors and incorrect predictions.
• First, we train a single-shot convolutional neural network (CNN) that produces multiple visual and geometric cues of interest: 2D bounding boxes, 2D keypoints of interest, coarse object orientations and object dimensions.
• Subsets of these cues are then used to poll probable ground planes from a pre-computed database of ground planes, to identify the "best fit" plane with highest consensus.