TY - GEN
T1 - ROCA
T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
AU - Gumeli, Can
AU - Dai, Angela
AU - Niebner, Matthias
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We present ROCA 11The code is made available at https://github.com/cangurneli/ROCA., a novel end-to-end approach that re-trieves and aligns 3D CAD models from a shape database to a single input image. This enables 3D perception of an ob-served scene from a 2D RGB observation, characterized as a lightweight, compact, clean CAD representation. Core to our approach is our differentiable alignment optimization based on dense 2D-3D object correspondences and Pro-crustes alignment. ROCA can thus provide a robust CAD alignment while simultaneously informing CAD retrieval by leveraging the 2D-3D correspondences to learn geometri-cally similar CAD models. Experiments on challenging, real-world imagery from ScanNet show that ROCA signif-icantly improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD alignment accuracy.
AB - We present ROCA 11The code is made available at https://github.com/cangurneli/ROCA., a novel end-to-end approach that re-trieves and aligns 3D CAD models from a shape database to a single input image. This enables 3D perception of an ob-served scene from a 2D RGB observation, characterized as a lightweight, compact, clean CAD representation. Core to our approach is our differentiable alignment optimization based on dense 2D-3D object correspondences and Pro-crustes alignment. ROCA can thus provide a robust CAD alignment while simultaneously informing CAD retrieval by leveraging the 2D-3D correspondences to learn geometri-cally similar CAD models. Experiments on challenging, real-world imagery from ScanNet show that ROCA signif-icantly improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD alignment accuracy.
KW - 3D from multi-view and sensors
KW - 3D from single images
KW - RGBD sensors and analytics
KW - Scene analysis and understanding
KW - Vision + graphics
UR - http://www.scopus.com/inward/record.url?scp=85140199702&partnerID=8YFLogxK
U2 - 10.1109/CVPR52688.2022.00399
DO - 10.1109/CVPR52688.2022.00399
M3 - Conference contribution
AN - SCOPUS:85140199702
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 4012
EP - 4021
BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PB - IEEE Computer Society
Y2 - 19 June 2022 through 24 June 2022
ER -