Abstract
Binary descriptors are widely used for multi-view matching and robotic navigation. However, their matching performance decreases considerably under severe scale and viewpoint changes in non-planar scenes. To overcome this problem, we propose to encode the varying appearance of selected 3D scene points tracked by a moving camera with compact spatio-temporal descriptors. To this end, we first track interest points and capture their temporal variations at multiple scales. Then, we validate feature tracks through 3D reconstruction and compress the temporal sequence of descriptors by encoding the most frequent and stable binary values. Finally, we determine multi-scale correspondences across views with a matching strategy that handles severe scale differences. The proposed spatio-temporal multi-scale approach is generic and can be used with a variety of binary descriptors. We show the effectiveness of the joint multi-scale extraction and temporal reduction through comparisons of different temporal reduction strategies and the application to several binary descriptors.