Semantic 3D indoor scene enhancement using guide words

We propose a novel framework for semantically enhancing a 3D indoor scene in agreement with a user-provided guide word. To do so, we make changes to furniture colors and place small objects in the scene. The relevance of specific furniture colors and small objects to each guide word is learned from a database of annotated images, taking into account both their frequency and specificity to that guide word. Enhancement suggestions are generated by optimizing a scoring function, which combines the relevance of both enhancement factors, i.e., furniture colors and small objects. During optimization, a submodular set function is adopted to ensure that a diverse set of enhancement suggestions is produced. Our experiments show that this framework can generate enhancement suggestions that are both compatible with the input guide word, and comparable to ones designed by humans.