A Multi-View Extension of the ICP algorithm

ABSTRACT Although the Iterative Closest Point (ICP) algorithm has been an extremely popular method for 3D points or surface registration, it can only be applied to two point sets at a time. By only registering two scans at a time, ICP fails to exploit the redundant information available in multiple scans that have overlapping regions. In this paper, we present a multi-view extension of the ICP algorithm by a method that simultaneously averages the redundant information available in the scans with overlapping regions. Variants of this method that carry out such simultaneous registration in a causal manner and that utilise the transitive property of point correspondences are also presented. The improved accuracy of this motion averaged approach in comparison with ICP and some multi-view methods is established through multiple tests. We also present results of our method applied to some well-known real datasets

Photometric Refinement of Depth Maps for Multi-albedo Objects

In this paper, we propose a novel uncalibrated photometric method for refining depth maps of multi-albedo objects obtained from consumer depth cameras like Kinect. Existing uncalibrated photometric methods either assume that the object has constant albedo or rely on segmenting images into constant albedo regions. The method of this paper does not require the constant albedo assumption and we believe it is the first work of its kind to handle objects with arbitrary albedo under uncalibrated illumination. We first robustly estimate a rank 3 approximation of the observed brightness matrix using an iterative reweighting method. Subsequently, we factorize this rank reduced brightness matrix into the corresponding lighting, albedo and surface normal components. The proposed factorization is shown to be convergent. We experimentally demonstrate the value of our approach by presenting highly accurate three-dimensional reconstructions of a wide variety of objects. Additionally, since any photometric method requires a radiometric calibration of the camera used, we also present a direct radiometric calibration technique for the infra-red camera of the structured-light stereo depth scanner. Unlike existing methods, this calibration technique does not depend on a known calibration object or on the properties of the scene illumination used

On Averaging Multiview Relations for 3D Scan Registration

In this paper, we present an extension of the iterative closest point (ICP) algorithm that simultaneously registers multiple 3D scans. While ICP fails to utilize the multiview constraints available, our method exploits the information redundancy in a set of 3D scans by using the averaging of relative motions. This averaging method utilizes the Lie group structure of motions, resulting in a 3D registration method that is both efficient and accurate. In addition, we present two variants of our approach, i.e., a method that solves for multiview 3D registration while obeying causality and a transitive correspondence variant that efficiently solves the correspondence problem across multiple scans. We present experimental results to characterize our method and explain its behavior as well as those of some other multiview registration methods in the literature. We establish the superior accuracy of our method in comparison to these multiview methods with registration results on a set of well-known real datasets of 3D scans

Efficient and Robust Large-Scale Rotation Averaging

In this paper we address the problem of robust and efficient averaging of relative 3D rotations. Apart from having an interesting geometric structure, robust rotation averaging addresses the need for a good initialization for large-scale optimization used in structure-from-motion pipelines. Such pipelines often use unstructured image datasets harvested from the internet thereby requiring an initialization method that is robust to outliers. Our approach works on the Lie group structure of 3D rotations and solves the problem of large-scale robust rotation averaging in two ways. Firstly, we use modern l(1) optimizers to carry out robust averaging of relative rotations that is efficient, scalable and robust to outliers. In addition, we also develop a two-step method that uses the l(1) solution as an initialisation for an iteratively reweighted least squares (IRLS) approach. These methods achieve excellent results on large-scale, real world datasets and significantly outperform existing methods, i.e. the state-of-the-art discrete-continuous optimization method of 3] as well as the Weiszfeld method of 8]. We demonstrate the efficacy of our method on two large-scale real world datasets and also provide the results of the two aforementioned methods for comparison

Robust Relative Rotation Averaging

This paper addresses the problem of robust and efficient relative rotation averaging in the context of large-scale Structure from Motion. Relative rotation averaging finds global or absolute rotations for a set of cameras from a set of observed relative rotations between pairs of cameras. We propose a generalized framework of relative rotation averaging that can use different robust loss functions and jointly optimizes for all the unknown camera rotations. Our method uses a quasi-Newton optimization which results in an efficient iteratively reweighted least squares (IRLS) formulation that works in the Lie algebra of the 3D rotation group. We demonstrate the performance of our approach on a number of large-scale data sets. We show that our method outperforms existing methods in the literature both in terms of speed and accuracy

Robust Feature-Preserving Denoising of 3D Point Clouds

The increased availability of point cloud data in recent years has lead to a concomitant requirement for high quality denoising methods. This is particularly the case with data obtained using depth cameras or from multi-view stereo reconstruction as both approaches result in noisy point clouds and include significant outliers. Most of the available denoising methods in the literature are not sufficiently robust to outliers and/or are unable to preserve finescale 3D features in the denoised representations. In this paper we propose an approach to point cloud denoising that is both robust to outliers and capable of preserving finescale 3D features. We identify and remove outliers by utilising a dissimilarity measure based on point positions and their corresponding normals. Subsequently, we use a robust approach to estimate surface point positions in a manner designed to preserve sharp and fine-scale 3D features. We demonstrate the efficacy of our approach and compare with similar methods in the literature by means of experiments on synthetic and real data including large-scale 3D reconstructions of heritage monuments