Multimodal Templates for Real-Time Detection of Texture-less Objects in Heavily Cluttered Scenes

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.

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Stefan Hinterstoisser, Stefan Holzer, Cedric Cagniart, Slobodan Ilic, Kurt Konolige, Nassir Navab, Vincent Lepetit
• Keywords: Commodity Hardware, Computer Vision, Dense Depth, ICCV 2011, Training Stage