Synthetically trained multi-view object class and viewpoint detection for advanced image retrieval

Abstract

This paper proposes a novel approach to multi-view object class and viewpoint detection for the retrieval of images showing one or several objects from a given viewpoint, a viewpoint range or any viewpoint in image databases. All detectors are trained exclusively on a few synthetic 3D models without any manual bounding-box, viewpoint or part annotation, making object class and viewpoint detection a scalable learning task. Previous work on this topic relies on the detection of object parts for each individual viewpoint, ignoring the responses of part detectors specific to other viewpoints. Instead, we explicitly exploit appearance ambiguities caused by spurious detections of parts under more than one viewpoint by combining all detector responses in a joint spatial pyramid encoding. We achieve state-of-the-art results in multi-view object class detection and viewpoint determination on current benchmarking data sets and demonstrate increased robustness to partial occlusion.