Semantic Segmentation
Semantic Segmentation of 3D Point Cloud to Virtually Manipulate Real Living Space
2018
Yuki Ishikawa, Ryo Hachiuma, Naoto Ienaga, Wakaba Kuno, Yuta Sugiura and Hideo Saito
This paper presents a method for the virtual manipulation of real living space using semantic segmentation of a 3D point cloud captured in the real world. We applied PointNet to segment each piece of furniture from the point cloud of a real indoor environment captured by moving an RGB-D camera. For semantic segmentation, we focused on local geometric information not used in PointNet, and we proposed a method to refine the class probability of labels attached to each point in PointNet’s output. The effectiveness of our method was experimentally confirmed. We then created 3D models of real-world furniture using a point cloud with corrected labels, and we virtually manipulated real living space using DollhouseVR, a layout system.