In this paper, we present a novel deep method to reconstruct a point cloud of an object from a single still image. Prior arts in the field struggle to reconstruct an accurate and scalable 3D model due to either the inefficient and expensive 3D representations, the dependency between the output and number of model parameters or the lack of a suitable computing operation. We propose to overcome these by deforming a random point cloud to the object shape through two steps: Feature blending and deformation. In the first step, the global and point-specific shape features extracted from a 2D object image are blended with the encoded feature of a randomly generated point cloud, and then this mixture is sent to the deformation step to produce the final representative point set of the object. In the deformation process, we introduce a new layer termed as GraphX that considers the inter-relationship between points like common graph convolutions but operates on unordered sets. Moreover, with a simple trick, the proposed model can generate an arbitrary-sized point cloud, which is the first deep method to do so. Extensive experiments verify that we outperform existing models and halve the state-of-the-art distance score in single image 3D reconstruction.
|Title of host publication||Proceedings - 2019 International Conference on Computer Vision, ICCV 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published - 2019 Oct|
|Event||17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of|
Duration: 2019 Oct 27 → 2019 Nov 2
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Conference||17th IEEE/CVF International Conference on Computer Vision, ICCV 2019|
|Country/Territory||Korea, Republic of|
|Period||19/10/27 → 19/11/2|
Bibliographical noteFunding Information:
This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1702-08.
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition