1 research outputs found
TVL1 Shape Approximation from Scattered 3D Data
With the emergence in 3D sensors such as laser scanners and 3D reconstruction from cameras, large 3D point
clouds can now be sampled from physical objects within a scene. The raw 3D samples delivered by these
sensors however, contain only a limite d degree of information about the environment the objects exist in,
which means that further geometrical high-level modelling is essential. In addition, issues like sparse data
measurements, noise, missing samples due to occlusion, and the inherently huge datasets involved in such
representations makes this task extremely challenging. This paper addresses these issues by presenting a new
3D shape modelling framework for samples acquired from 3D sensor. Motivated by the success of nonlinear
kernel-based approximation techniques in the statistics domain, existing methods using radial basis functions
are applied to 3D object shape approximation. The task is framed as an optimization problem and is extended
using non-smooth L1 total variation regularization. Appropriate convex energy functionals are constructed and
solved by applying the Alternating Direction Method of Multipliers approach, which is then extended using
Gauss-Seidel iterations. This significantly lowers the computational complexity involved in generating 3D
shape from 3D samples, while both numerical and qualitative analysis confirms the superior shape modelling
performance of this new framework compared with existing 3D shape reconstruction techniques