Learning to efficiently detect repeatable interest points in depth data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
3D object detection with multiple kinects
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
View-Invariant object detection by matching 3d contours
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Boosting masked dominant orientation templates for efficient object detection
Computer Vision and Image Understanding
Bootstrapping a robot's kinematic model
Robotics and Autonomous Systems
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We present a method for detecting 3D objects using multi-modalities. While it is generic, we demonstrate it on the combination of an image and a dense depth map which give complementary object information. It works in real-time, under heavy clutter, does not require a time consuming training stage, and can handle untextured objects. It is based on an efficient representation of templates that capture the different modalities, and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities.