What is it about?

In 2007 we started to build software for real-time object detection and pose estimation for tracking purposes. Since then, we developed many new approaches that either need only locally well textured areas of the object to be tracked or no texture at all. On this website you can download our software and test it for your own research applications. From time to time, we will provide an updated version of the software packets including latest developments and/or new features.

Related Publications

Dominant Orientation Templates for Real-Time Detection of Texture-Less Objects

Authors: S. Hinterstoisser, V. Lepetit, S. Ilic, P. Fua, N. Navab

We present a method for real-time 3D object detection that does not require a time consuming training stage, and can handle untextured objects. At its core, is a novel template representation that is designed to be robust to small image transformations. This robustness based on dominant gradient orientations lets us test only a small subset of all possible pixel locations when parsing the image, and to represent a 3D object with a limited set of templates. We show that together with a binary representation that makes evaluation very fast and a branch-and-bound approach to efficiently scan the image, it can detect untextured objects in complex situations and provide their 3D pose in real-time.

Published in: IEEE Conference on Computer Vision and Pattern Recognition, 2010

Real-Time Learning of Accurate Patch Rectification

Authors: S. Hinterstoisser, O. Kutter, N. Navab, P. Fua, V. Lepetit

Recent work showed that learning-based patch rectification methods are both faster and more reliable than affine region methods. Unfortunately, their performance improvements are founded in a computationally expensive offline learning stage, which is not possible for applications such as SLAM. In this paper we propose an approach whose training stage is fast enough to be performed at run-time without the loss of accuracy or robustness. To this end, we developed a very fast method to compute the mean appearances of the feature points over sets of small variations that span the range of possible camera viewpoints. Then, by simply matching incoming feature points against these mean appearances, we get a coarse estimate of the viewpoint that is refined afterwards. Because there is no need to compute descriptors for the input image, the method is very fast at run-time. We demonstrate our approach on trackingby- detection for SLAM, real-time object detection and pose estimation applications.

Published in: IEEE Conference on Computer Vision and Pattern Recognition, 2009

Online Learning of Patch Perspective Rectification for Efficient Object Detection

Authors: S. Hinterstoisser, S. Benhimane, N. Navab, P. Fua, V. Lepetit

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.

Published in: IEEE Conference on Computer Vision and Pattern Recognition, 2008