MF3D: Model-free 3D semantic scene parsing

Abstract

We present a novel model-free method for online 3D semantic scene parsing from video sequences. MF3D (Model-Free 3D) is different from conventional methods for 3D scene parsing in that voxel labelling is approached via search-based label transfer instead of discriminative classification. This non-parametric approach makes MF3D easy to scale with an online growth in the database, as no model re-training is required with the addition of new examples or categories. Experimental results on the KITTI benchmark demonstrate that our model-free approach enables accurate online 3D scene parsing while retaining extensibility to new categories. In addition, we show that unsupervised binary encoding (hashing) techniques can be easily incorporated into our framework for scalability to larger databases.