TY - GEN

T1 - Pointflow

T2 - 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019

AU - Yang, Guandao

AU - Huang, Xun

AU - Hao, Zekun

AU - Liu, Ming Yu

AU - Belongie, Serge

AU - Hariharan, Bharath

N1 - Funding Information: This work was supported in part by a research gift from Magic Leap. Xun Huang was supported by NVIDIA Graduate Fellowship. Publisher Copyright: © 2019 IEEE.

PY - 2019/10

Y1 - 2019/10

N2 - As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds becomes crucial. Despite the recent success of deep learning models in discriminative tasks of point clouds, generating point clouds remains challenging. This paper proposes a principled probabilistic framework to generate 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape. This formulation allows us to both sample shapes and sample an arbitrary number of points from a shape. Our generative model, named PointFlow, learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Empirically, we demonstrate that PointFlow achieves state-of-the-art performance in point cloud generation. We additionally show that our model can faithfully reconstruct point clouds and learn useful representations in an unsupervised manner. The code is available at https://github.com/stevenygd/PointFlow.

AB - As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds becomes crucial. Despite the recent success of deep learning models in discriminative tasks of point clouds, generating point clouds remains challenging. This paper proposes a principled probabilistic framework to generate 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape. This formulation allows us to both sample shapes and sample an arbitrary number of points from a shape. Our generative model, named PointFlow, learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Empirically, we demonstrate that PointFlow achieves state-of-the-art performance in point cloud generation. We additionally show that our model can faithfully reconstruct point clouds and learn useful representations in an unsupervised manner. The code is available at https://github.com/stevenygd/PointFlow.

UR - http://www.scopus.com/inward/record.url?scp=85081931570&partnerID=8YFLogxK

U2 - 10.1109/ICCV.2019.00464

DO - 10.1109/ICCV.2019.00464

M3 - Conference article

AN - SCOPUS:85081931570

SP - 4540

EP - 4549

JO - Proceedings of the IEEE International Conference on Computer Vision

JF - Proceedings of the IEEE International Conference on Computer Vision

SN - 1550-5499

Y2 - 27 October 2019 through 2 November 2019

ER -