StarMap for Category-Agnostic Keypoint and Viewpoint Estimation

Abstract. Semantic keypoints provide concise abstractions for a variety of visual understanding tasks. Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels. As a result, these representation is not suitable when objects have a varying number of parts, e.g. chairs with varying number of legs. We propose a category-agnostic keypoint representation encoded with their 3D locations in the canonical object views. Our intuition is that the 3D locations of the keypoints in canonical object views contain rich semantic and compositional information. Our representation thus consists of a single channel, multi-peak heatmap (StarMap) for all the keypoints and their corresponding features as 3D locations in the canonical object view (CanViewFeature) defined for each category. Not only is our representation flexible, but we also demonstrate competitive performance in keypoint detection and localization compared to category-specific stateof-the-art methods. Additionally, we show that when augmented with an additional depth channel (DepthMap) to lift the 2D keypoints to 3D, our representation can achieve state-of-the-art results in viewpoint estimation. Finally, we demonstrate that each individual component of our framework can be used on the task of human pose estimation to simplify the state-of-the-art architecture.