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The capability of customer intensity cameras extends way past leisure and gaming, to real-world advertisement functions. This authoritative textual content studies the scope and effect of this quickly growing to be box, describing the main promising Kinect-based examine actions, discussing major present demanding situations, and showcasing intriguing purposes. good points: offers contributions from a world choice of preeminent specialists of their fields, from either educational and company learn; addresses the vintage challenge of multi-view geometry of ways to correlate photographs from diversified viewpoints to concurrently estimate digital camera poses and international issues; examines human pose estimation utilizing video-rate intensity pictures for gaming, movement seize, 3D human physique scans, and hand pose popularity for signal language parsing; offers a evaluation of methods to numerous attractiveness difficulties, together with type and example studying of items, and human job reputation; with a Foreword by way of Dr. Jamie Shotton.
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Extra info for Consumer Depth Cameras for Computer Vision: Research Topics and Applications
Pattern Anal. Mach. Intell. : SLAM combining ToF and high-resolution cameras. In: IEEE Workshop on Applications of Computer Vision, pp. : A nearest neighbor data structure for graphics hardware. : Accelerating nearest neighbor search on manycore systems. : Object modelling by registration of multiple range images. Image Vis. Comput. : A volumetric method for building complex models from range images. In: Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, pp. 303–312. : Color constrained ICP for registration of large unstructured 3D color data sets.
For the nearest neighbor search, being a major bottleneck in terms of runtime, CPU architectures have shown to benefit from space-partitioning data structures like k-d trees . In contrast to algorithmic improvements, hardware acceleration techniques are increasingly attracting the attention of the community. Garcia et al. have shown that a GPU-based brute-force implementation outperforms a CPU-based k-d tree . The reason for this lies in the fact that the brute-force primitive can be implemented efficiently using techniques known from the well understood problem of GPU-based matrix–matrix multiplication.
Compared to a conventional ICP that relies on the pure 3-D geometry [4, 9], incorporating photometric appearance as a complementary source of information is advantageous in cases of non-salient surface topology, recall Fig. 1 and the experimental results in related work . Approaches that combine dense geometric point associations with a sparse set of correspondences derived from local photometric features are limited to interactive frame-rates, as feature extraction is computationally expensive even if performed on the GPU .