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In this paper we present a new monochromatic pattern for a robust structured light coding based on the spatial neighborhood scheme using the M-array approach. The proposed pattern is robust as it allows a high error rate characterized by an average Hamming distance higher than 6. We tackle the design problem with the definition of a small set of symbols associated to simple geometrical primitives. One of these primitives embeds the local orientation of the pattern. This is helpful while performing the search for the relevant neighborhood during the decoding process. The aim of this work is to use this pattern for the real-time 3-D reconstruction of dynamic scenes, particularly in endoscopic surgery, with fast and reliable detection and decoding stages. Ongoing results are presented to assess both the capabilities of the proposed pattern and the reliable decoding algorithm with projections onto simple 3-D scenes and onto internal structures of a pig abdomen.

Contemporary endoscopic Simultaneous Localization And Mapping (SLAM) methods accurately compute endoscope poses, however, they only provide a sparse 3D reconstruction that poorly describes the surgical scene. We propose a novel dense SLAM method whose qualities are: 1) Monocular, requiring only RGB images of a hand-held monocular endoscope. 2) Fast, providing endoscope positional tracking and 3D scene reconstruction, running in parallel threads. 3) Dense, yielding an accurate dense reconstruction. 4) Robust, to the severe illumination changes, poor texture and small deformations that are typical in endoscopy. 5) Self-contained, without needing any fiducials nor external tracking devices, therefore it can be smoothly integrated into the surgical workflow. It works as follows. Firstly, accurate cluster frame poses are estimated using the sparse SLAM feature matches. The system segments clusters of video frames according to a parallax criteria. Next, dense matches between cluster frames are computed in parallel by a variational approach that combines Zero Mean Normalized Cross Correlation (ZNCC) and a gradient Huber norm regularizer. This combination copes with challenging lighting and textures at an affordable time budget on a modern GPU. It can outperform pure stereo reconstructions because the frames cluster can provide larger parallax from the endoscope's motion. We provide an extensive experimental validation on real sequences of the porcine abdominal cavity, both in-vivo and exvivo. We also show a qualitative evaluation on human liver. Additionally, we show a comparison with other dense SLAM methods showing the performance gain in terms of accuracy, density and computation time.