3,784 research outputs found
A comparative study of breast surface reconstruction for aesthetic outcome assessment
Breast cancer is the most prevalent cancer type in women, and while its
survival rate is generally high the aesthetic outcome is an increasingly
important factor when evaluating different treatment alternatives. 3D scanning
and reconstruction techniques offer a flexible tool for building detailed and
accurate 3D breast models that can be used both pre-operatively for surgical
planning and post-operatively for aesthetic evaluation. This paper aims at
comparing the accuracy of low-cost 3D scanning technologies with the
significantly more expensive state-of-the-art 3D commercial scanners in the
context of breast 3D reconstruction. We present results from 28 synthetic and
clinical RGBD sequences, including 12 unique patients and an anthropomorphic
phantom demonstrating the applicability of low-cost RGBD sensors to real
clinical cases. Body deformation and homogeneous skin texture pose challenges
to the studied reconstruction systems. Although these should be addressed
appropriately if higher model quality is warranted, we observe that low-cost
sensors are able to obtain valuable reconstructions comparable to the
state-of-the-art within an error margin of 3 mm.Comment: This paper has been accepted to MICCAI201
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
Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation
Accounting for 26% of all new cancer cases worldwide, breast cancer remains
the most common form of cancer in women. Although early breast cancer has a
favourable long-term prognosis, roughly a third of patients suffer from a
suboptimal aesthetic outcome despite breast conserving cancer treatment.
Clinical-quality 3D modelling of the breast surface therefore assumes an
increasingly important role in advancing treatment planning, prediction and
evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive
and either infrastructure-heavy or subject to motion artefacts. In this paper
we employ a single consumer-grade RGBD camera with an ICP-based registration
approach to jointly align all points from a sequence of depth images
non-rigidly. Subtle body deformation due to postural sway and respiration is
successfully mitigated leading to a higher geometric accuracy through
regularised locally affine transformations. We present results from 6 clinical
cases where our method compares well with the gold standard and outperforms a
previous approach. We show that our method produces better reconstructions
qualitatively by visual assessment and quantitatively by consistently obtaining
lower landmark error scores and yielding more accurate breast volume estimates
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations
Size, weight, and power constrained platforms impose constraints on
computational resources that introduce unique challenges in implementing
localization algorithms. We present a framework to perform fast localization on
such platforms enabled by the compressive capabilities of Gaussian Mixture
Model representations of point cloud data. Given raw structural data from a
depth sensor and pitch and roll estimates from an on-board attitude reference
system, a multi-hypothesis particle filter localizes the vehicle by exploiting
the likelihood of the data originating from the mixture model. We demonstrate
analysis of this likelihood in the vicinity of the ground truth pose and detail
its utilization in a particle filter-based vehicle localization strategy, and
later present results of real-time implementations on a desktop system and an
off-the-shelf embedded platform that outperform localization results from
running a state-of-the-art algorithm on the same environment
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