Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition

Existing scene understanding systems mainly focus on recognizing the visible parts of a scene, ignoring the intact appearance of physical objects in the real-world. Concurrently, image completion has aimed to create plausible appearance for the invisible regions, but requires a manual mask as input.

Pluralistic Free-Form Image Completion

Video Abstract Most image completion methods produce only one result for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for pluralistic image completion the task of generating multiple diverse and plausible solutions for image completion.

Multi-class indoor semantic segmentation with deep structured model

Indoor semantic segmentation plays a critical role in many applications, such as intelligent robots. However, multi-class recognition is still challenging, especially for pixel-level indoor semantic labeling. In this paper, a novel deep structured …

Learning aggregated features and optimizing model for semantic labeling

Semantic labeling for indoor scenes has been extensively developed with the wide availability of affordable RGB-D sensors. However, it is still a challenging task for multi-class recognition, especially for “small” objects. In this paper, a novel …

Learning contextual information for indoor semantic segmentation"