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Titel:

Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Zakharov, S.; Kehl, W.; Bhargava, A.; Gaidon, A.
Abstract:
We present an automatic annotation pipeline to recover 9D cuboids and 3D shape from pre-trained off-the-shelf 2D detectors and sparse LIDAR data. Our autolabeling method solves this challenging ill-posed inverse problem by relying on learned shape priors and optimization of geometric and physical parameters. To that end, we propose a novel differentiable shape renderer over signed distance fields (SDF), which we leverage in combination with normalized object coordinate spaces (NOCS). Initially t...     »
Stichworte:
CAMP,CAMPComputerVision,ComputerVision,CVPR,CVPR2020,CNN,Autolabeling,Deep Learning,deeplearning
Kongress- / Buchtitel:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Jahr:
2020
Seiten:
12224--12233
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