Combining Inertial Navigation and ICP for Real-time 3D Surface Reconstruction

We present a novel, physically-motivated method for surface reconstruction that can recover smooth surfaces from noisy and sparse data sets, without using orientation information..

From acquired reflectance data in the form of a 6-D Bidirectional Texture Function (BTF), the proposed algorithm creates an efficient volumetric representation in the form of a stack

We start by computing a low res- olution surface reconstruction from roughly aligned scans (which we expect as input) and use this surface as a pro- totype surface to align all

Our method com- putes a smooth density function f from the input data points and uses ridge extraction methods to determine a connected surface of maximal local density which will

To recover more accurate and topologically controlled surface representation of organs from diagnostic images, several ad- vanced 3D segmentation techniques are applied

voxel confidence measure α i ∈ [0 : 1] is introduced. It is based on the Euclidean distance from the voxel centre to the nearest input point d i E. Confidence values in voxels

Real-time Virtual Navigation Information Update The virtual navigation information, e.g., a prior diagnostic MRI, shall be updated in real time according to the output of

To recover more accurate and topologically controlled surface representation of organs from diagnostic images, several ad- vanced 3D segmentation techniques are applied