Appearance and Geometry Fusion for Enhanced Dense 3D Alignment
This work proposes a novel RGB-D feature descriptor called Binary Appearance and Shape Elements (BASE) that efficiently combines intensity and shape information to improve the discriminative power and enable an enhanced and faster matching process. The new descriptor is used to align a set of RGB point clouds to generate dense three dimensional models of indoor environments. We compare the performance of state-of-the-art feature descriptors with the proposed descriptor for scene alignment through the registration of multiple indoor textured depth maps. Experimental results show that the proposed descriptor outperforms the other approaches in computational cost, memory consumption and match quality. Additionally, experiments based on cloud alignment show that the BASE descriptor is suitable to be used in the registration of RBG-D data even when the environment is partially illuminated.