Multi-scale tensor voting for feature extraction from unstructured point clouds

Identifying sharp features in a 3D model is essential for shape analysis, matching and a wide range of geometry processing applications. This paper presents a new method based on the tensor voting theory to extract sharp features from an unstructured point cloud which may contain random noise, outliers and artifacts. Our method first takes the voting tensors at every point using the corresponding neighborhoods and computes the feature weight to infer the local structure via eigenvalue analysis of the tensor. The optimal scale for a point is automatically determined by observing the feature weight variation in order to deal with both a noisy smooth region and a sharp edge. We finally extract the points at sharp features using adaptive thresholding of the feature weight and the feature completion process. The multi-scale tensor voting of a given point set improves noise sensitivity and scale dependency of an input model. We demonstrate the strength of the proposed method in terms of efficiency and robustness by comparing it with other feature detection algorithms.

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► We develop a new feature detection method based on the multi-scale tensor voting.
► The multi-scale scheme improves noise sensitivity and scale dependency of point data.
► Each point has the optimal scale to analyze the intrinsic structure around the point.
► Tensor voting for shape analysis can handle the data with the unknown dimensionality.