Shape Reconstruction with Uncertainty

Abstract This paper presents a general Surface Reconstruction framework which encapsulates the uncertainty of the sam-pled data, making no assumption on the shape of the surface to be reconstructed. Starting from the input points(either points clouds or multiple range images), an Estimated Existence Function (EEF) is built which modelsthe space in which the desired surface could exist and, by the extraction of EEF critical points, the surface isreconstructed. The nal goal is the development of a generic framework able to adapt the result to different kindsof additional information coming from multiple sensors. Categories and Subject Descriptors (according to ACM CCS) : I.3.3 [Computer Graphics]: Shape Modeling, Uncer-tain data, Multi-sensor Data Fusion 1. Introduction 3D scanning devices are becoming more and more availableand affordable. Thanks to modern acquisition technologies,heterogeneous data can be acquired from multiple acquisi-tion sensors, which often incorporate information about un-certainty of the data sampling process. Surface reconstruc-tion techniques designed over a specic sensor often takeinto account uncertainty during the reconstruction process,but they are limited to work with a single device. On thecontrary, general techniques that can process data comingfrom different sensors usually disregard much part of sensor-specic information, and seldom take into account uncer-tainty.The basic concept of our approach is