3,263 research outputs found
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
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
In this paper we present ActiveStereoNet, the first deep learning solution
for active stereo systems. Due to the lack of ground truth, our method is fully
self-supervised, yet it produces precise depth with a subpixel precision of
of a pixel; it does not suffer from the common over-smoothing issues;
it preserves the edges; and it explicitly handles occlusions. We introduce a
novel reconstruction loss that is more robust to noise and texture-less
patches, and is invariant to illumination changes. The proposed loss is
optimized using a window-based cost aggregation with an adaptive support weight
scheme. This cost aggregation is edge-preserving and smooths the loss function,
which is key to allow the network to reach compelling results. Finally we show
how the task of predicting invalid regions, such as occlusions, can be trained
end-to-end without ground-truth. This component is crucial to reduce blur and
particularly improves predictions along depth discontinuities. Extensive
quantitatively and qualitatively evaluations on real and synthetic data
demonstrate state of the art results in many challenging scenes.Comment: Accepted by ECCV2018, Oral Presentation, Main paper + Supplementary
Material
Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts
This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
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
Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
In the last decade, many medical companies and research groups have tried to
convert passive capsule endoscopes as an emerging and minimally invasive
diagnostic technology into actively steerable endoscopic capsule robots which
will provide more intuitive disease detection, targeted drug delivery and
biopsy-like operations in the gastrointestinal(GI) tract. In this study, we
introduce a fully unsupervised, real-time odometry and depth learner for
monocular endoscopic capsule robots. We establish the supervision by warping
view sequences and assigning the re-projection minimization to the loss
function, which we adopt in multi-view pose estimation and single-view depth
estimation network. Detailed quantitative and qualitative analyses of the
proposed framework performed on non-rigidly deformable ex-vivo porcine stomach
datasets proves the effectiveness of the method in terms of motion estimation
and depth recovery.Comment: submitted to IROS 201
Stereo uparivanje iz video isjeÄŤka
This paper proposes a novel method for stereo matching which is based on combination of active and passive stereo 3D reconstruction approaches. A laser line is used to scan the reconstructed scene and a stereo camera pair is used for the image acquisition. Each image pixel is scanned at a specific scan time so that the intensity time patterns of the correspondent pixels are highly correlated. This yield in highly confident and accurate disparity map calculation and also allows the reconstruction of poorly textured as well as the extremely textured surface which are very hard to deal with using the conventional passive stereo approaches. The occluded regions are also detected successfully. This method is not computationally intensive and can be used for turning the smartphone into the practical 3D scanner as presented in this work.Ovim radom predstavljena je nova metoda stereo uparivanja temeljena na kombinaciji aktivnog i pasivnog stereo pristupa. Rekonstruirana scena skenirana je laserskom linijom, dok se par stereo kamera koristi za akviziciju video isječka. Svaki slikovni element rekonstruirane scene skeniran je laserskom linijom u određenom trenutku stoga su profili intenziteta svjetline u vremenskoj domeni izrazito korelirani za slikovne elemente lijeve i desne kamere koji odgovaraju istom slikovnom element rekonstruirane scene. Stoga je rezultat predstavljene metode određivanje stereo parova slikovnih elemenata s visokom pouzdanošću. Nadalje, predstavljena metoda omogućuje rekonstruiranje izrazito slabo odnosno izrazito intenzivno teksturiranih scena što je često veoma teško postići korištenjem konvencionalnih metoda stereo 3D rekonstrukcije. Metoda je jednostavna te ju je moguće implementirati na sustavima ograničenih računarskih resursa, stoga je iznimno pogodna za primjenu na mobilnim platformama primjerice pametnim telefonima
Reliable fusion of ToF and stereo depth driven by confidence measures
In this paper we propose a framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and stereo vision system. Initially, depth data acquired by the ToF camera are upsampled by an ad-hoc algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity map is obtained using the Semi- Global Matching stereo algorithm. Reliable confidence measures are extracted for both the ToF and stereo depth data. In particular, ToF confidence also accounts for the mixed-pixel effect and the stereo confidence accounts for the relationship between the pointwise matching costs and the cost obtained by the semi-global optimization. Finally, the two depth maps are synergically fused by enforcing the local consistency of depth data accounting for the confidence of the two data sources at each location. Experimental results clearly show that the proposed method produces accurate high resolution depth maps and outperforms the compared fusion algorithms
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