816 research outputs found
Anatomical Mirroring: Real-time User-specific Anatomy in Motion Using a Commodity Depth Camera
International audienceThis paper presents a mirror-like augmented reality (AR) system to display the internal anatomy of a user. Using a single Microsoft V2.0 Kinect, we animate in real-time a user-specific internal anatomy according to the user’s motion and we superimpose it onto the user’s color map. The user can visualize his anatomy moving as if he was able to look inside his own body in real-time. A new calibration procedure to set up and attach a user-specific anatomy to the Kinect body tracking skeleton is introduced. At calibration time, the bone lengths are estimated using a set of poses. By using Kinect data as input, the practical limitation of skin correspondance in prior work is overcome. The generic 3D anatomical model is attached to the internal anatomy registration skeleton, and warped on the depth image using a novel elastic deformer, subject to a closest-point registration force and anatomical constraints. The noise in Kinect outputs precludes any realistic human display. Therefore, a novel filter to reconstruct plausible motions based onfixed length bones as well as realistic angular degrees of freedom (DOFs) and limits is introduced to enforce anatomical plausibility. Anatomical constraints applied to the Kinect body tracking skeleton joints are used to maximize the physical plausibility of the anatomy motion, while minimizing the distance to the raw data. At run-time,a simulation loop is used to attract the bones towards the raw data, and skinning shaders efficiently drag the resulting anatomy to the user’s tracked motion.Our user-specific internal anatomy model is validated by comparing the skeleton with segmented MRI images. A user study is established to evaluate the believability of the animated anatomy
Performance Factors in Neurosurgical Simulation and Augmented Reality Image Guidance
Virtual reality surgical simulators have seen widespread adoption in an effort to provide safe, cost-effective and realistic practice of surgical skills. However, the majority of these simulators focus on training low-level technical skills, providing only prototypical surgical cases. For many complex procedures, this approach is deficient in representing anatomical variations that present clinically, failing to challenge users’ higher-level cognitive skills important for navigation and targeting. Surgical simulators offer the means to not only simulate any case conceivable, but to test novel approaches and examine factors that influence performance. Unfortunately, there is a void in the literature surrounding these questions. This thesis was motivated by the need to expand the role of surgical simulators to provide users with clinically relevant scenarios and evaluate human performance in relation to image guidance technologies, patient-specific anatomy, and cognitive abilities. To this end, various tools and methodologies were developed to examine cognitive abilities and knowledge, simulate procedures, and guide complex interventions all within a neurosurgical context. The first chapter provides an introduction to the material. The second chapter describes the development and evaluation of a virtual anatomical training and examination tool. The results suggest that learning occurs and that spatial reasoning ability is an important performance predictor, but subordinate to anatomical knowledge. The third chapter outlines development of automation tools to enable efficient simulation studies and data management. In the fourth chapter, subjects perform abstract targeting tasks on ellipsoid targets with and without augmented reality guidance. While the guidance tool improved accuracy, performance with the tool was strongly tied to target depth estimation – an important consideration for implementation and training with similar guidance tools. In the fifth chapter, neurosurgically experienced subjects were recruited to perform simulated ventriculostomies. Results showed anatomical variations influence performance and could impact outcome. Augmented reality guidance showed no marked improvement in performance, but exhibited a mild learning curve, indicating that additional training may be warranted. The final chapter summarizes the work presented. Our results and novel evaluative methodologies lay the groundwork for further investigation into simulators as versatile research tools to explore performance factors in simulated surgical procedures
Learning 3D Human Pose from Structure and Motion
3D human pose estimation from a single image is a challenging problem,
especially for in-the-wild settings due to the lack of 3D annotated data. We
propose two anatomically inspired loss functions and use them with a
weakly-supervised learning framework to jointly learn from large-scale
in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal
network that exploits temporal and structural cues present in predicted pose
sequences to temporally harmonize the pose estimations. We carefully analyze
the proposed contributions through loss surface visualizations and sensitivity
analysis to facilitate deeper understanding of their working mechanism. Our
complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M
and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics
card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
THE UNIVERSAL MEDIA BOOK
We explore the integration of projected imagery with a physical book that acts as a tangible interface to multimedia data. Using a camera and projector pair, a tracking framework is presented wherein the 3D position of planar pages are monitored as they are turned back and forth by a user, and data is correctly warped and projected onto each page at interactive rates to provide the user with an intuitive mixed-reality experience. The book pages are blank, so traditional camera-based approaches to tracking physical features on the display surface do not apply. Instead, in each frame, feature points are independently extracted from the camera and projector images, and matched to recover the geometry of the pages in motion. The book can be loaded with multimedia content, including images and videos. In addition, volumetric datasets can be explored by removing a page from the book and using it as a tool to navigate through a virtual 3D volume
Virtual Presence for Medical Procedures
As medical training becomes more and more complex, with students being expected to learn
increasingly specialized and sophisticated procedures, the current practice of having students
physically observe all procedures is becoming increasingly difficult. Some procedures are
exceedingly rare, while others may rely on specialized equipment not available to the student's
institution. Additionally, some procedures can be fast-paced, and critical details might be
overlooked in such a hectic environment. We present an application solution that records the
procedure with multiple cameras, reconstructs the 3D environment and people frame-by-frame,
then utilizes virtual reality (VR) to allow the student to walk through the reconstruction of the
procedure through time. We also include several post-reconstruction enhancements, such as
video playback controls, scene annotations, and introducing new 3D models into the
environment. While we present our solution in the context of medical training, our system is
general enough to be applicable in a wide variety of training scenarios.Bachelor of Scienc
Development of an augmented reality guided computer assisted orthopaedic surgery system
Previously held under moratorium from 1st December 2016 until 1st December 2021.This body of work documents the developed of a proof of concept augmented reality
guided computer assisted orthopaedic surgery system – ARgCAOS.
After initial investigation a visible-spectrum single camera tool-mounted tracking
system based upon fiducial planar markers was implemented. The use of
visible-spectrum cameras, as opposed to the infra-red cameras typically used by
surgical tracking systems, allowed the captured image to be streamed to a display in
an intelligible fashion. The tracking information defined the location of physical
objects relative to the camera. Therefore, this information allowed virtual models to
be overlaid onto the camera image. This produced a convincing augmented
experience, whereby the virtual objects appeared to be within the physical world,
moving with both the camera and markers as expected of physical objects.
Analysis of the first generation system identified both accuracy and graphical
inadequacies, prompting the development of a second generation system. This too
was based upon a tool-mounted fiducial marker system, and improved performance
to near-millimetre probing accuracy. A resection system was incorporated into the
system, and utilising the tracking information controlled resection was performed,
producing sub-millimetre accuracies.
Several complications resulted from the tool-mounted approach. Therefore, a third
generation system was developed. This final generation deployed a stereoscopic
visible-spectrum camera system affixed to a head-mounted display worn by the user.
The system allowed the augmentation of the natural view of the user, providing
convincing and immersive three dimensional augmented guidance, with probing and
resection accuracies of 0.55±0.04 and 0.34±0.04 mm, respectively.This body of work documents the developed of a proof of concept augmented reality
guided computer assisted orthopaedic surgery system – ARgCAOS.
After initial investigation a visible-spectrum single camera tool-mounted tracking
system based upon fiducial planar markers was implemented. The use of
visible-spectrum cameras, as opposed to the infra-red cameras typically used by
surgical tracking systems, allowed the captured image to be streamed to a display in
an intelligible fashion. The tracking information defined the location of physical
objects relative to the camera. Therefore, this information allowed virtual models to
be overlaid onto the camera image. This produced a convincing augmented
experience, whereby the virtual objects appeared to be within the physical world,
moving with both the camera and markers as expected of physical objects.
Analysis of the first generation system identified both accuracy and graphical
inadequacies, prompting the development of a second generation system. This too
was based upon a tool-mounted fiducial marker system, and improved performance
to near-millimetre probing accuracy. A resection system was incorporated into the
system, and utilising the tracking information controlled resection was performed,
producing sub-millimetre accuracies.
Several complications resulted from the tool-mounted approach. Therefore, a third
generation system was developed. This final generation deployed a stereoscopic
visible-spectrum camera system affixed to a head-mounted display worn by the user.
The system allowed the augmentation of the natural view of the user, providing
convincing and immersive three dimensional augmented guidance, with probing and
resection accuracies of 0.55±0.04 and 0.34±0.04 mm, respectively
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