Hashmod: A Hashing Method for Scalable 3D Object Detection
This addresses the problem of efficient 3D object recognition for robotics or AR applications, but it is incremental as it builds on an existing 2D approach.
The paper tackles scalable 3D object detection and pose estimation in RGB-D data by extending a 2D hashing method to 3D, achieving high accuracy and sublinear complexity with runtime improvements over state-of-the-art.
We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashing techniques to match these views against the input frame in a scalable way. While a similar approach already exists for 2D detection, we show how to extend it to estimate the 3D pose of the detected objects. In particular, we explore different hashing strategies and identify the one which is more suitable to our problem. We show empirically that the complexity of our method is sublinear with the number of objects and we enable detection and pose estimation of many 3D objects with high accuracy while outperforming the state-of-the-art in terms of runtime.