3D Car Instance Understanding

2 papers with code • 1 benchmarks • 2 datasets

3D Car Instance Understanding is the task of estimating properties (e.g.translation, rotation and shape) of a moving or parked vehicle on the road.

( Image credit: Occlusion-Net )

Datasets


Greatest papers with code

Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

dineshreddy91/Occlusion_Net CVPR 2019

Central to this work is a trifocal tensor loss that provides indirect self-supervision for occluded keypoint locations that are visible in other views of the object.

3D Car Instance Understanding 3D Object Reconstruction From A Single Image +2

GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision

lkeab/gsnet ECCV 2020

GSNet utilizes a unique four-way feature extraction and fusion scheme and directly regresses 6DoF poses and shapes in a single forward pass.

3D Car Instance Understanding 3D Pose Estimation +11