3D Object Detection with Multiple Kinects

Categorizing and localizing multiple objects in 3D space is a challenging but essential task for many robotics and assisted living applications. While RGB cameras as well as depth information have been widely explored in computer vision there is surprisingly little recent work combining multiple cameras and depth information. Given the recent emergence of consumer depth cameras such as Kinect we explore how multiple cameras and active depth sensors can be used to tackle the challenge of 3D object detection. More specifically we generate point clouds from the depth information of multiple registered cameras and use the VFH descriptor [20] to describe them. For color images we employ the DPM [3] and combine both approaches with a simple voting approach across multiple cameras. On the large RGB-D dataset [12] we show improved performance for object classification on multi-camera point clouds and object detection on color images, respectively. To evaluate the benefit of joining color and depth information of multiple cameras, we recorded a novel dataset with four Kinects showing significant improvements over a DPM baseline for 9 object classes aggregated in challenging scenes. In contrast to related datasets our dataset provides color and depth information recorded with multiple Kinects and requires localizing and categorizing multiple objects in 3D space. In order to foster research in this field, the dataset, including annotations, is available on our web page.