Depth-Encoded Hough Voting for joint object detection and shape recovery

Detecting objects, estimating their pose and recovering 3D shape information is a critical problem in many vision and robotics applications. This paper addresses the above needs by proposing a new method called DEHV - Depth-Encoded Hough Voting detection scheme. Inspired by the Hough voting scheme introduced in [13], DEHV incorporates depth information into the process of learning distributions of image features (patches) representing an object category. DEHV takes advantage of the interplay between the scale of each object patch in the image and its distance (depth) from the corresponding physical patch attached to the 3D object. In training, we use various views of an object using a 2D image and its associated depth map (which we assume is available in learning). In testing, DEHV jointly detects objects, infers their categories, estimates their pose, and infers/decodes objects depth maps from either a single image (when no depth maps are available in testing) or a single image augmen...

• Type: Conference
• Year: 2010
• Where: ECCV
• Keywords: Computer Vision, Depth Map, ECCV 2010, Hough Voting, Object Category