3,640 research outputs found
Robust and Fast 3D Scan Alignment using Mutual Information
This paper presents a mutual information (MI) based algorithm for the
estimation of full 6-degree-of-freedom (DOF) rigid body transformation between
two overlapping point clouds. We first divide the scene into a 3D voxel grid
and define simple to compute features for each voxel in the scan. The two scans
that need to be aligned are considered as a collection of these features and
the MI between these voxelized features is maximized to obtain the correct
alignment of scans. We have implemented our method with various simple point
cloud features (such as number of points in voxel, variance of z-height in
voxel) and compared the performance of the proposed method with existing
point-to-point and point-to- distribution registration methods. We show that
our approach has an efficient and fast parallel implementation on GPU, and
evaluate the robustness and speed of the proposed algorithm on two real-world
datasets which have variety of dynamic scenes from different environments
Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations
This article introduces a new physics-based method for rigid point set
alignment called Fast Gravitational Approach (FGA). In FGA, the source and
target point sets are interpreted as rigid particle swarms with masses
interacting in a globally multiply-linked manner while moving in a simulated
gravitational force field. The optimal alignment is obtained by explicit
modeling of forces acting on the particles as well as their velocities and
displacements with second-order ordinary differential equations of motion.
Additional alignment cues (point-based or geometric features, and other
boundary conditions) can be integrated into FGA through particle masses. We
propose a smooth-particle mass function for point mass initialization, which
improves robustness to noise and structural discontinuities. To avoid
prohibitive quadratic complexity of all-to-all point interactions, we adapt a
Barnes-Hut tree for accelerated force computation and achieve quasilinear
computational complexity. We show that the new method class has characteristics
not found in previous alignment methods such as efficient handling of partial
overlaps, inhomogeneous point sampling densities, and coping with large point
clouds with reduced runtime compared to the state of the art. Experiments show
that our method performs on par with or outperforms all compared competing
non-deep-learning-based and general-purpose techniques (which do not assume the
availability of training data and a scene prior) in resolving transformations
for LiDAR data and gains state-of-the-art accuracy and speed when coping with
different types of data disturbances.Comment: 18 pages, 18 figures and two table
A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation
Intrinsic isometric shape matching has become the standard approach for pose
invariant correspondence estimation among deformable shapes. Most existing
approaches assume global consistency, i.e., the metric structure of the whole
manifold must not change significantly. While global isometric matching is well
understood, only a few heuristic solutions are known for partial matching.
Partial matching is particularly important for robustness to topological noise
(incomplete data and contacts), which is a common problem in real-world 3D
scanner data. In this paper, we introduce a new approach to partial, intrinsic
isometric matching. Our method is based on the observation that isometries are
fully determined by purely local information: a map of a single point and its
tangent space fixes an isometry for both global and the partial maps. From this
idea, we develop a new representation for partial isometric maps based on
equivalence classes of correspondences between pairs of points and their
tangent spaces. From this, we derive a local propagation algorithm that find
such mappings efficiently. In contrast to previous heuristics based on RANSAC
or expectation maximization, our method is based on a simple and sound
theoretical model and fully deterministic. We apply our approach to register
partial point clouds and compare it to the state-of-the-art methods, where we
obtain significant improvements over global methods for real-world data and
stronger guarantees than previous heuristic partial matching algorithms.Comment: 17 pages, 12 figure
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