DISCOVERING CORRESPONDENCE AMONG IMAGE SETS WITH PROJECTION VIEW PRESERVATION FOR 3D OBJECT DETECTION IN POINT CLOUDS

We propose a method for detecting objects that correspond to given three-dimensional (3D) point clouds in a scene. We regard the 3D object detection as a series of optimal matching of the object and scene images that are obtained by projecting point clouds into multiple viewpoints. The key novelty of the proposed method is to introduce a constraint imposed by the spatial relationship among the image-projection directions for the object point clouds, to discover the optimal matching of the projected image sets. This constraint allows to evaluate the appearance consistency of the object in multi-viewpoint scene images. Thus, image-projection directions can be effective cues to detect objects even in cluttered scenes, where previous methods are not effective. We estimate the image-projection directions for the object point clouds by applying principal component analysis to the object point clouds and hence include highly discriminative image features. Then, we back-project reliable matching results, which are retrieved from the image set correspondence, into 3D space to achieve a point-wise object detection. Experiments using public datasets demonstrate the effectiveness and performance of the proposed method.