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3D Reconstruction using Point-Based Fusion

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Since real-time range cameras (Time-of-Flight or Kinect cameras) become more and more available in the market, new 3D applications arise such as scene understanding, augmented reality or human-computer interaction. Recently, a cheap 3D reconstruction method using the kinect camera has been proposed. But its extension for ToF cameras has not yet been demonstrated and will bring further challenges due to ToF camera properties (e.g. low resolution, complex noise behaviour, etc.).

This research focuses on developing a fast 3D geometry reconstruction based on a simple points representation that allows adaptive resolution and workspace. Our method uses the iterative closest point (ICP) to track the camera motion (describing all data in the same reference), fuse all similar data to a single vertex list (model) and visualizes this model using surfel rendering. A surfel is composed of a 3D position (center), an orientation (normal) and a size (radius). Note that the data fusion module assumes a simple gaussian distribution.

Since no volumetric data is used, our method can be easily extended to dynamics environment. Dynamic objects are detected using the matching measurement provided by the ICP algorithm. A GPU based region growing method is later applied to the outlier map and the input data with respect to depth and normal similarities in order to fully segment dynamic objects.

The project was a collaboration with Microsoft Research Cambridge and University College London.

This talk is part of the Microsoft Research Cambridge, public talks series.

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