Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

In this paper we develop a surgical system using the da Vinci research kit (dVRK) that is capable of autonomously searching for tumors and dynamically displaying the tumor location using augmented reality. Such a system has the potential to quickly reveal the location and shape of tumors and visually overlay that information to reduce the cognitive overload of the surgeon. We believe that our approach is one of the first to incorporate state-of-the-art methods in registration, force sensing and tumor localization into a unified surgical system. First, the preoperative model is registered to the intra-operative scene using a Bingham distribution-based filtering approach. An active level set estimation is then used to find the location and the shape of the tumors. We use a recently developed miniature force sensor to perform the palpation. The estimated stiffness map is then dynamically overlaid onto the registered preoperative model of the organ. We demonstrate the efficacy of our system by performing experiments on phantom prostate models with embedded stiff inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018

OSGAR: a scene graph with uncertain transformations

An important problem for augmented reality is registration error. No system can be perfectly tracked, calibrated or modeled. As a result, the overlaid graphics are not aligned perfectly with objects in the physical world. This can be distracting, annoying or confusing. In this paper, we propose a method for mitigating the effects of registration errors that enables application developers to build dynamically adaptive AR displays. Our solution is implemented in a programming toolkit called OSGAR. Built upon OpenSceneGraph (OSG), OSGAR statistically characterizes registration errors, monitors those errors and, when a set of criteria are met, dynamically adapts the display to mitigate the effects of the errors. Because the architecture is based on a scene graph, it provides a simple, familiar and intuitive environment for application developers. We describe the components of OSGAR, discuss how several proposed methods for error registration can be implemented, and illustrate its use through a set of examples

Augmented reality usage for prototyping speed up

The first part of the article describes our approach for solution of this problem by means of Augmented Reality. The merging of the real world model and digital objects allows streamline the work with the model and speed up the whole production phase significantly. The main advantage of augmented reality is the possibility of direct manipulation with the scene using a portable digital camera. Also adding digital objects into the scene could be done using identification markers placed on the surface of the model. Therefore it is not necessary to work with special input devices and lose the contact with the real world model. Adjustments are done directly on the model. The key problem of outlined solution is the ability of identification of an object within the camera picture and its replacement with the digital object. The second part of the article is focused especially on the identification of exact position and orientation of the marker within the picture. The identification marker is generalized into the triple of points which represents a general plane in space. There is discussed the space identification of these points and the description of representation of their position and orientation be means of transformation matrix. This matrix is used for rendering of the graphical objects (e. g. in OpenGL and Direct3D).Comment: Keywords: augmented reality, prototyping, pose estimation, transformation matri

Augmented Reality Markerless Multi-Image Outdoor Tracking System for the Historical Buildings on Parliament Hill

[EN] Augmented Reality (AR) applications have experienced extraordinary growth recently, evolving into a well-established method for the dissemination and communication of content related to cultural heritage¿including education. AR applications have been used in museums and gallery exhibitions and virtual reconstructions of historic interiors. However, the circumstances of an outdoor environment can be problematic. This paper presents a methodology to develop immersive AR applications based on the recognition of outdoor buildings. To demonstrate this methodology, a case study focused on the Parliament Buildings National Historic Site in Ottawa, Canada has been conducted. The site is currently undergoing a multiyear rehabilitation program that will make access to parts of this national monument inaccessible to the public. AR experiences, including simulated photo merging of historic and present content, are proposed as one tool that can enrich the Parliament Hill visit during the rehabilitation. Outdoor AR experiences are limited by factors, such as variable lighting (and shadows) conditions, caused by changes in the environment (objects height and orientation, obstructions, occlusions), the weather, and the time of day. This paper proposes a workflow to solve some of these issues from a multi-image tracking approach.This work has been developed under the framework of the New Paradigms/New Tools for Heritage Conservation in Canada, a project funded through the Social Sciences and Humanities Research Council of Canada (SSHRC).Blanco-Pons, S.; Carrión-Ruiz, B.; Duong, M.; Chartrand, J.; Fai, S.; Lerma, JL. (2019). Augmented Reality Markerless Multi-Image Outdoor Tracking System for the Historical Buildings on Parliament Hill. Sustainability. 11(16):1-15. https://doi.org/10.3390/su11164268S1151116Bekele, M. K., Pierdicca, R., Frontoni, E., Malinverni, E. S., & Gain, J. (2018). A Survey of Augmented, Virtual, and Mixed Reality for Cultural Heritage. Journal on Computing and Cultural Heritage, 11(2), 1-36. doi:10.1145/3145534Gimeno, J., Portalés, C., Coma, I., Fernández, M., & Martínez, B. (2017). Combining traditional and indirect augmented reality for indoor crowded environments. A case study on the Casa Batlló museum. Computers & Graphics, 69, 92-103. doi:10.1016/j.cag.2017.09.001Kolivand, H., El Rhalibi, A., Shahrizal Sunar, M., & Saba, T. (2018). ReVitAge: Realistic virtual heritage taking shadows and sky illumination into account. Journal of Cultural Heritage, 32, 166-175. doi:10.1016/j.culher.2018.01.020Amakawa, J., & Westin, J. (2017). New Philadelphia: using augmented reality to interpret slavery and reconstruction era historical sites. International Journal of Heritage Studies, 24(3), 315-331. doi:10.1080/13527258.2017.1378909Kim, J.-B., & Park, C. (2011). Development of Mobile AR Tour Application for the National Palace Museum of Korea. Lecture Notes in Computer Science, 55-60. doi:10.1007/978-3-642-22021-0_7Barrile, V., Fotia, A., Bilotta, G., & De Carlo, D. (2019). Integration of geomatics methodologies and creation of a cultural heritage app using augmented reality. Virtual Archaeology Review, 10(20), 40. doi:10.4995/var.2019.10361Analysis of Tracking Accuracy for Single-Camera Square-Marker-Based Tracking. In Third Workshop on Virtual and Augmented Reality of the GI-Fachgruppe VR/AR, Koblenz, Germany, 2006http://campar.in.tum.de/Chair/PublicationDetail?pub=pentenrieder2006giCirulis, A., & Brigmanis, K. B. (2013). 3D Outdoor Augmented Reality for Architecture and Urban Planning. Procedia Computer Science, 25, 71-79. doi:10.1016/j.procs.2013.11.009You, S., Neumann, U., & Azuma, R. (1999). Orientation tracking for outdoor augmented reality registration. IEEE Computer Graphics and Applications, 19(6), 36-42. doi:10.1109/38.799738Wither, J., Tsai, Y.-T., & Azuma, R. (2011). Indirect augmented reality. Computers & Graphics, 35(4), 810-822. doi:10.1016/j.cag.2011.04.010Radkowski, R., & Oliver, J. (2013). Natural Feature Tracking Augmented Reality for On-Site Assembly Assistance Systems. Lecture Notes in Computer Science, 281-290. doi:10.1007/978-3-642-39420-1_30Rao, J., Qiao, Y., Ren, F., Wang, J., & Du, Q. (2017). A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization. Sensors, 17(9), 1951. doi:10.3390/s17091951Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1993). Mesh optimization. Proceedings of the 20th annual conference on Computer graphics and interactive techniques - SIGGRAPH ’93. doi:10.1145/166117.166119Rossignac, J., & Borrel, P. (1993). Multi-resolution 3D approximations for rendering complex scenes. Modeling in Computer Graphics, 455-465. doi:10.1007/978-3-642-78114-8_29Gross, M. H., Staadt, O. G., & Gatti, R. (1996). Efficient triangular surface approximations using wavelets and quadtree data structures. IEEE Transactions on Visualization and Computer Graphics, 2(2), 130-143. doi:10.1109/2945.506225Botsch, M., Pauly, M., Rossl, C., Bischoff, S., & Kobbelt, L. (2006). Geometric modeling based on triangle meshes. ACM SIGGRAPH 2006 Courses on - SIGGRAPH ’06. doi:10.1145/1185657.1185839Pietroni, N., Tarini, M., & Cignoni, P. (2010). Almost Isometric Mesh Parameterization through Abstract Domains. IEEE Transactions on Visualization and Computer Graphics, 16(4), 621-635. doi:10.1109/tvcg.2009.96Khan, D., Yan, D.-M., Ding, F., Zhuang, Y., & Zhang, X. (2018). Surface remeshing with robust user-guided segmentation. Computational Visual Media, 4(2), 113-122. doi:10.1007/s41095-018-0107-yGuidi, G., Russo, M., Ercoli, S., Remondino, F., Rizzi, A., & Menna, F. (2009). A Multi-Resolution Methodology for the 3D Modeling of Large and Complex Archeological Areas. International Journal of Architectural Computing, 7(1), 39-55. doi:10.1260/147807709788549439Remondino, F., & El-Hakim, S. (2006). Image-based 3D Modelling: A Review. The Photogrammetric Record, 21(115), 269-291. doi:10.1111/j.1477-9730.2006.00383.xBruno, F., Bruno, S., De Sensi, G., Luchi, M.-L., Mancuso, S., & Muzzupappa, M. (2010). From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition. Journal of Cultural Heritage, 11(1), 42-49. doi:10.1016/j.culher.2009.02.006Unity, The Photogrammetry Workflowhttps://unity.com/solutions/photogrammetry.Blanco, S., Carrión, B., & Lerma, J. L. (2016). REVIEW OF AUGMENTED REALITY AND VIRTUAL REALITY TECHNIQUES IN ROCK ART. Proceedings of the ARQUEOLÓGICA 2.0 8th International Congress on Archaeology, Computer Graphics, Cultural Heritage and Innovation. doi:10.4995/arqueologica8.2016.3561Behzadan, A. H., & Kamat, V. R. (2010). Scalable Algorithm for Resolving Incorrect Occlusion in Dynamic Augmented Reality Engineering Environments. Computer-Aided Civil and Infrastructure Engineering, 25(1), 3-19. doi:10.1111/j.1467-8667.2009.00601.xTian, Y., Long, Y., Xia, D., Yao, H., & Zhang, J. (2015). Handling occlusions in augmented reality based on 3D reconstruction method. Neurocomputing, 156, 96-104. doi:10.1016/j.neucom.2014.12.081Tian, Y., Guan, T., & Wang, C. (2010). Real-Time Occlusion Handling in Augmented Reality Based on an Object Tracking Approach. Sensors, 10(4), 2885-2900. doi:10.3390/s10040288

Fine-To-Coarse Global Registration of RGB-D Scans

RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a "fine-to-coarse" global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. During experiments with this benchmark, we find that our fine-to-coarse algorithm registers long RGB-D sequences better than previous methods