3D Motion Estimation By Evidence Gathering

In this paper we introduce an algorithm for 3D motion estimation in point clouds that is based on Chasles’ kinematic theorem. The proposed algorithm estimates 3D motion parameters directly from the data by exploiting the geometry of rigid transformation using an evidence gathering technique in a Hough-voting-like approach. The algorithm provides an alternative to the feature description and matching pipelines commonly used by numerous 3D object recognition and registration algorithms, as it does not involve keypoint detection and feature descriptor computation and matching. To the best of our knowledge, this is the first research to use kinematics theorems in an evidence gathering framework for motion estimation and surface matching without the use of any given correspondences. Moreover, we propose a method for voting for 3D motion parameters using a one-dimensional accumulator space, which enables voting for motion parameters more efficiently than other methods that use up to 7-dimensional accumulator spaces

3D moving object reconstruction by temporal accumulation

Much progress has been made recently in the development of 3D acquisition technologies, which increased the availability of low-cost 3D sensors, such as the Microsoft Kinect. This promotes a wide variety of computer vision applications needing object recognition and 3D reconstruction. We present a novel algorithm for full 3D reconstruction of unknown rotating objects in 2.5D point cloud sequences, such as those generated by 3D sensors. Our algorithm incorporates structural and temporal motion information to build 3D models of moving objects and is based on motion compensated temporal accumulation. The proposed algorithm requires only the fixed centre or axis of rotation, unlike other 3D reconstruction methods, it does not require key point detection, feature description, correspondence matching, provided object models or any geometric information about the object. Moreover, our algorithm integrally estimates the best rigid transformation parameters for registration, applies surface resembling, reduces noise and estimates the optimum angular velocity of the rotating object

On evidence gathering in 3D point clouds of static and moving objects

The recent and considerable progress in 3D sensing technologies mandates the development of efficient algorithms to process the sensed data. Many of these algorithms are based on computing and matching of 3D feature descriptors in order to estimate point correspondences between 3D datasets.The dependency on 3D feature description and computation can be a significant limitation to many 3D perception tasks; the fact that there are a variety of criteria used to describe 3D features, such as surface normals and curvature, makes feature-based approaches sensitive to noise and occlusion. In many cases, such as smooth surfaces, computation of feature descriptors can be non-informative. Moreover, the process of computing and matching features requires more computational overhead than using points directly.On the other hand, there has not been much focus on employing evidence gathering frameworks to obtain solutions for 3D perception problems. Evidence gathering approaches, which use data directly, have proved to provide robust performance against noise and occlusion. More importantly, evidence gathering approaches do not require initialisation or training, and avoid the need to solve the correspondence problem.The capability to detect, extract and reconstruct 3D bjects without relying on feature matching and estimating correspondences between 3D datasets has not been thoroughly investigated, and is certainly desirable and has many practical applications.In this thesis we present theoretical formulations and practical solutions to 3D perceptual tasks, that are based on evidence gathering. We propose a new 3D reconstruction algorithm for rotating objects that is based on motion-compensated temporal accumulation. We also propose two fast and robust Hough Transform based algorithms for 3D static parametric object detection and 3D moving parametric object extraction.Furthermore, we introduce two algorithms for 3D motion parameter estimation that are based on Reuleaux's and Chasles' kinematic theorems. The proposed algorithms estimate 3D motion parameters directly from the data by exploiting the geometry of rigid transformation. Moreover, they provide an alternative to the both local and global feature description and matching pipelines commonly used by numerous 3D object recognition and registration algorithms.Our objective is to provide new means for understanding static and dynamic scenes, captured by new 3D sensing technologies as we believe that these technologies will be dominant in the perception field as they are going under rapid development. We provide alternative solutions to commonly used feature based approaches by using new evidence gathering based methods for the processing of 3D range data.<br/

Detecting moving spheres in 3D point clouds via the 3D velocity Hough transform

We present a new approach to extracting moving spheres from a sequence of 3D point clouds. The new 3D velocity Hough Transform (3DVHT) incorporates motion parameters in addition to structural parameters in an evidence gathering process to accurately detect moving spheres at any given point cloud from the sequence. We demonstrate its capability to detect spheres which are obscured within the sequence of point clouds, which conventional approaches cannot achieve. We apply our algorithm on real and synthetic data and demonstrate the ability of detecting fully occluded spheres by exploiting inter-frame correlation within the 3D point cloud sequence

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population