1,721,567 research outputs found
Real-time and scalable incremental segmentation on dense SLAM
No full textThis work proposes a real-time segmentation method for 3D point clouds obtained via Simultaneous Localization And Mapping (SLAM). The proposed method incrementally merges segments obtained from each input depth image in a unified global model using a SLAM framework. Differently from all other approaches, our method is able to yield segmentation of scenes reconstructed from multiple views in real-time, with a complexity that does not depend on the size of the global model. At the same time, it is also general, as it can be deployed with any frame-wise segmentation approach as well as any SLAM algorithm. We validate our proposal by a comparison with the state of the art in terms of computational efficiency and accuracy on a benchmark dataset, as well as by showing how our method can enable real-time segmentation from reconstructions of diverse real indoor environments
Repeatable Local Coordinate Frames for 3D Human Motion Tracking: From Rigid to Non-rigid
No full textLocal coordinate frame (LCF) is a key component deployed in most 3D descriptors for invariant representations of 3D surfaces. This paper addresses the problem of attaching a LCF to non-rigidly deforming objects, in particular humanoid surfaces, with the application of recovering correspondences between the template model and input data for 3D human motion tracking. We facilitate this by extending two current LCF paradigms for rigid surface matching to the non-rigid case. Such an adaptation is motivated by the assumption that interpolating locally rigid movements often amounts to smooth globally non-rigid deformations. Both approaches leverage spatial distributions, based on signed distance and principal component analysis, respectively. Furthermore, we advocate a new strategy that incorporates multiple LCF candidates. This way we relax the requirement of perfectly repeatable LCFs, and yet still achieve improved data-model associations. Ground truth for non-rigid LCFs are synthetically generated by interpolating locally-rigidly transformed LCFs. Therefore, the proposed methods can be evaluated extensively in terms of repeatability of LCFs, robustness on estimating correspondences, and accuracy of final tracking results. All the experiments demonstrate the benefits of the proposed methods with respect to the state-of-the-art
Preface to the Proceedings of Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015
No full text
Determination of Pelvic Orientation from Ultrasound Images Using Patch-SSMs and a Hierarchical Speed of Sound Compensation Strategy
No full textIn the field of computer assisted orthopedic surgery (CAOS) the anterior pelvic plane (APP) is a common concept to determine the pelvic orientation by digitizing distinct pelvic landmarks. As percutaneous palpation is - especially for obese patients - known to be error-prone, B-mode ultrasound (US) imaging could provide an alternative means. Several concepts of using ultrasound imaging to determine the APP landmarks have been introduced. In this paper we present a novel technique, which uses local patch statistical shape models (SSMs) and a hierarchical speed of sound compensation strategy for an accurate determination of the APP. These patches are independently matched and instantiated with respect to associated point clouds derived from the acquired ultrasound images. Potential inaccuracies due to the assumption of a constant speed of sound are compensated by an extended reconstruction scheme. We validated our method with in-vitro studies using a plastic bone covered with a soft-tissue simulation phantom and with a preliminary cadaver trial
A versatile learning-based 3d temporal tracker: Scalable, robust, online
No full textThis paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time. Compared to the state of the art aimed at the same goal, our algorithm holds important attributes such as high robustness against holes and occlusion, low computational cost of both learning and tracking stages, and low memory consumption. These are obtained (a) by a novel formulation of the learning strategy, based on a dense sampling of the camera viewpoints and learning independent trees from a single image for each camera view, as well as, (b) by an insightful occlusion handling strategy that enforces the forest to recognize the object's local and global structures. Due to these attributes, we report state-of-the-art tracking accuracy on benchmark datasets, and accomplish remarkable scalability with the number of targets, being able to simultaneously track the pose of over a hundred objects at 30~fps with an off-the-shelf CPU. In addition, the fast learning time enables us to extend our algorithm as a robust online tracker for model-free 3D objects under different viewpoints and appearance changes as demonstrated by the experiments
A Combined Generalized and Subject-Specific 3D Head Pose Estimation
No full textWe propose a real-time method for 3D head pose estimation from RGB-D sequences. Our algorithm relies on a Random Forest framework that is able to regress the head pose at every frame in a temporal tracking manner. Such framework is learned once from a generic dataset of 3D head models and refined online to adapt the forest to the specific characteristics of each subject. Through the qualitative experiments under different conditions, it demonstrates remarkable properties in terms of robustness to occlusions, computational efficiency and capacity of handling a variety of challenging head poses. In addition, it also outperforms the state of the art on the reference benchmark dataset with regards to the accuracy of the estimated head poses
- …
