资源分类
》 资源 》论文》Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene

Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene

2019-10-15 | |  78 |   63 |   0
Abstract The goal of this paper is to take a single 2D image of a scene and recover the 3D structure in terms of a small set of factors: a layout representing the enclosing surfaces as well as a set of objects represented in terms of shape and pose. We propose a convolutional neural network-based approach to predict this representation and benchmark it on a large dataset of indoor scenes. Our experiments evaluate a number of practical design questions, demonstrate that we can infer this representation, and quantitatively and qualitatively demonstrate its merits compared to alternate representations

上一篇：Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition?

下一篇：Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...