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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.

We show that the simultaneous estimation of keypoint identities and poses is more reliable than the two separate steps undertaken by previous approaches. A simple linear classifier coupled with linear predictors trained during a learning phase appears to be sufficient for this task. The retrieved poses are subpixel accurate due to the linear predictors. We demonstrate the advantages of our approach on real-time 3D object detection and tracking applications. Thanks to the high accuracy, one single keypoint is often enough to precisely estimate the object pose. As a result, we can deal in real-time with objects that are significantly less textured than the ones required by state-of-the-art methods.

For a large class of applications, there is time to train the system. In this paper, we propose a learning-based approach to patch perspective rectification, and show that it is both faster and more reliable than state-of-the-art ad hoc affine region detection methods. Our method performs in three steps. First, a classifier provides for every keypoint not only its identity, but also a first estimate of its transformation. This estimate allows carrying out, in the second step, an accurate perspective rectification using linear predictors. We show that both the classifier and the linear predictors can be trained online, which makes the approach convenient. The last step is a fast verification –made possible by the accurate perspective rectification– of the patch identity and its sub-pixel precision position estimation. We test our approach on real-time 3D object detection and tracking applications. We show that we can use the estimated perspective rectifications to determine the object pose and as a result, we need much fewer correspondences to obtain a precise pose estimation.

We propose a method that dramatically improves the performance of template-based matching in terms of size of convergence region and computation time. This is done by selecting a subset of the template that verifies the assumption (made during optimization) of linearity or quadraticity with respect to the motion parameters. We call these subsets linear or quadratic subsets. While subset selection approaches have already been proposed, they generally do not attempt to provide linear or quadratic subsets and rely on heuristics such as texturedness. Because a naive search for the optimal subset would result in a combinatorial explosion for large templates, we propose a simple algorithm that does not aim for the optimal subset but provides a very good linear or quadratic subset at low cost, even for large templates. Simulation results and experiments with real sequences show the superiority of the proposed method compared to existing subset selection approaches.

We present a fully automated approach to camera registration for augmented reality systems. It relies on purely passive vision techniques to solve the initialization and real-time tracking problems, given a rough CAD model of parts of the real scene. It does not require a controlled environment, for example placing markers. It handles arbitrarily complex models, occlusions, large camera displacements and drastic aspect changes. This is made possible by two major contributions: the first one is a fast recognition method that detects the known part of the scene, registers the camera with respect to it, and initializes a real-time tracker, which is the second contribution. Our tracker eliminates drift and jitter by merging the information from preceding frames in a traditional recursive tracking fashion with that of a very limited number of key-frames created off-line. In the rare instances where it fails, for example because of large occlusion, it detects the failure and reinvokes the initialization procedure. We present experimental results on several different kinds of objects and scenes