Learning pose-invariant 3D object reconstruction from single-view images

摘要

Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data. However, most work in this direction requires multi-view images for each object instance as training supervision, which oftentimes does not apply in practice. In this paper, we relax the common multi-view assumption and explore a more challenging yet more realistic setup of learning 3D shape from only single-view images. The major difficulty lies in insufficient constraints that can be provided by single view images, which leads to the problem of pose entanglement in learned shape space. As a result, reconstructed shapes vary along input pose and have poor accuracy. We address this problem by taking a novel domain adaptation perspective, and propose an effective adversarial domain confusion method to learn pose-disentangled compact shape space. Experiments on single-view reconstruction show effectiveness in solving pose entanglement, and the proposed method achieves on-par reconstruction accuracy with state-of-the-art with higher efficiency.

出版物
Neurocomputing
彭勃
彭勃
副研究员
王伟
王伟
副研究员、硕导

主要从事多媒体内容安全、人工智能安全、多模态内容分析与理解等方面的研究工作。

董晶
董晶
研究员、硕导

主要从事多媒体内容安全、人工智能安全、多模态内容分析与理解等方面的研究工作。详情访问:http://cripac.ia.ac.cn/people/jdong

谭铁牛
谭铁牛
研究员,博导

主要从事图像处理、计算机视觉和模式识别等相关领域的研究工作,目前的研究主要集中在生物特征识别、图像视频理解和信息内容安全等三个方向。