3D Car Instance Understanding
3 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 )
Benchmarks
These leaderboards are used to track progress in 3D Car Instance Understanding
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
ApolloCar3D
CarFusion
Most implemented papers
Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks
dineshreddy91/Occlusion_Net
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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.
GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
lkeab/gsnet
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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.
BAAM: Monocular 3D Pose and Shape Reconstruction With Bi-Contextual Attention Module and Attention-Guided Modeling
A novel monocular 3D pose and shape reconstruction algorithm, based on bi-contextual attention and attention-guided modeling (BAAM), is proposed in this work.
