Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality

Real-time occlusion handling is a major problem in outdoor mixed reality system because it requires great computational cost mainly due to the complexity of the scene. Using only segmentation, it is difficult to accurately render a virtual object occluded by complex objects such as trees, bushes etc. In this paper, we propose a novel occlusion handling method for real-time, outdoor, and omni-directional mixed reality system using only the information from a monocular image sequence. We first present a semantic segmentation scheme for predicting the amount of visibility for different type of objects in the scene. We also simultaneously calculate a foreground probability map using depth estimation derived from optical flow. Finally, we combine the segmentation result and the probability map to render the computer generated object and the real scene using a visibility-based rendering method. Our results show great improvement in handling occlusions compared to existing blending based methods

A new head-mounted display-based augmented reality system in neurosurgical oncology: a study on phantom

Purpose: Benefits of minimally invasive neurosurgery mandate the development of ergonomic paradigms for neuronavigation. Augmented Reality (AR) systems can overcome the shortcomings of commercial neuronavigators. The aim of this work is to apply a novel AR system, based on a head-mounted stereoscopic video see-through display, as an aid in complex neurological lesion targeting. Effectiveness was investigated on a newly designed patient-specific head mannequin featuring an anatomically realistic brain phantom with embedded synthetically created tumors and eloquent areas. Materials and methods: A two-phase evaluation process was adopted in a simulated small tumor resection adjacent to Brocaâ\u80\u99s area. Phase I involved nine subjects without neurosurgical training in performing spatial judgment tasks. In Phase II, three surgeons were involved in assessing the effectiveness of the AR-neuronavigator in performing brain tumor targeting on a patient-specific head phantom. Results: Phase I revealed the ability of the AR scene to evoke depth perception under different visualization modalities. Phase II confirmed the potentialities of the AR-neuronavigator in aiding the determination of the optimal surgical access to the surgical target. Conclusions: The AR-neuronavigator is intuitive, easy-to-use, and provides three-dimensional augmented information in a perceptually-correct way. The system proved to be effective in guiding skin incision, craniotomy, and lesion targeting. The preliminary results encourage a structured study to prove clinical effectiveness. Moreover, our testing platform might be used to facilitate training in brain tumour resection procedures

Real-Time Occlusion Handling in Augmented Reality Based on an Object Tracking Approach

To produce a realistic augmentation in Augmented Reality, the correct relative positions of real objects and virtual objects are very important. In this paper, we propose a novel real-time occlusion handling method based on an object tracking approach. Our method is divided into three steps: selection of the occluding object, object tracking and occlusion handling. The user selects the occluding object using an interactive segmentation method. The contour of the selected object is then tracked in the subsequent frames in real-time. In the occlusion handling step, all the pixels on the tracked object are redrawn on the unprocessed augmented image to produce a new synthesized image in which the relative position between the real and virtual object is correct. The proposed method has several advantages. First, it is robust and stable, since it remains effective when the camera is moved through large changes of viewing angles and volumes or when the object and the background have similar colors. Second, it is fast, since the real object can be tracked in real-time. Last, a smoothing technique provides seamless merging between the augmented and virtual object. Several experiments are provided to validate the performance of the proposed method

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

Virtual Occlusions Through Implicit Depth

For augmented reality (AR), it is important that virtual assets appear to 'sit among' real world objects. The virtual element should variously occlude and be occluded by real matter, based on a plausible depth ordering. This occlusion should be consistent over time as the viewer's camera moves. Unfortunately, small mistakes in the estimated scene depth can ruin the downstream occlusion mask, and thereby the AR illusion. Especially in real-time settings, depths inferred near boundaries or across time can be inconsistent. In this paper, we challenge the need for depth-regression as an intermediate step. We instead propose an implicit model for depth and use that to predict the occlusion mask directly. The inputs to our network are one or more color images, plus the known depths of any virtual geometry. We show how our occlusion predictions are more accurate and more temporally stable than predictions derived from traditional depth-estimation models. We obtain state-of-the-art occlusion results on the challenging ScanNetv2 dataset and superior qualitative results on real scenes