In this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved.
Abstract. In this paper, we propose a method that constructs a high-resolution depth image with high quality from a low-resolution depth image that is noisy and contains holes. We believe that the high-resolution depth map is generated by sparse linear combination of atoms from an overcomplete dictionary, and the low-resolution depth map are the samples from the high-resolution depth map. Under Bayesian framework, we find the optimal sparse coefficient vector that represents the high-resolution map best. Comprehensive quantitative comparisons show that our method outperforms existing approaches when applied on Middlebury dataset, and qualitative comparison on real scenes indicates that our algorithm performs best.
Research on monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration Defence Technology 16, 158 (2020); Application of fast factorized back-projection algorithm for high-resolution highly squinted airborne SAR imaging SCIENCE CHINA Information Sciences 60, 062301 (2017); Machine-learning-based high-resolution DOA measurement and robust directional modulation for hybrid analog-digital massive MIMO transceiver SCIENCE CHINA Information Sciences 63, 180302 (2020); Preserving details in semantics-aware context for scene parsing SCIENCE CHINA Information Sciences 63, 120106 (2020);. RESEARCH PAPER .
InfTouch is a multi-touch technique that tracks any objects with diffuse surface. This technique provides tracking at 25 Hz, and is able to work reliably in hazardous environment. The integration to over-sized displays is simple and convenient. InfTouch has no border, which makes it a good free-air interaction tool. The commercial applications prove the usefulness of our technique.
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