4,453 research outputs found
A bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of non-rigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated.
An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current non-rigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth.Peer ReviewedPostprint (author's final draft
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
Time-Contrastive Networks: Self-Supervised Learning from Video
We propose a self-supervised approach for learning representations and
robotic behaviors entirely from unlabeled videos recorded from multiple
viewpoints, and study how this representation can be used in two robotic
imitation settings: imitating object interactions from videos of humans, and
imitating human poses. Imitation of human behavior requires a
viewpoint-invariant representation that captures the relationships between
end-effectors (hands or robot grippers) and the environment, object attributes,
and body pose. We train our representations using a metric learning loss, where
multiple simultaneous viewpoints of the same observation are attracted in the
embedding space, while being repelled from temporal neighbors which are often
visually similar but functionally different. In other words, the model
simultaneously learns to recognize what is common between different-looking
images, and what is different between similar-looking images. This signal
causes our model to discover attributes that do not change across viewpoint,
but do change across time, while ignoring nuisance variables such as
occlusions, motion blur, lighting and background. We demonstrate that this
representation can be used by a robot to directly mimic human poses without an
explicit correspondence, and that it can be used as a reward function within a
reinforcement learning algorithm. While representations are learned from an
unlabeled collection of task-related videos, robot behaviors such as pouring
are learned by watching a single 3rd-person demonstration by a human. Reward
functions obtained by following the human demonstrations under the learned
representation enable efficient reinforcement learning that is practical for
real-world robotic systems. Video results, open-source code and dataset are
available at https://sermanet.github.io/imitat
Unsupervised Learning of Depth and Ego-Motion from Video
We present an unsupervised learning framework for the task of monocular depth
and camera motion estimation from unstructured video sequences. We achieve this
by simultaneously training depth and camera pose estimation networks using the
task of view synthesis as the supervisory signal. The networks are thus coupled
via the view synthesis objective during training, but can be applied
independently at test time. Empirical evaluation on the KITTI dataset
demonstrates the effectiveness of our approach: 1) monocular depth performing
comparably with supervised methods that use either ground-truth pose or depth
for training, and 2) pose estimation performing favorably with established SLAM
systems under comparable input settings.Comment: Accepted to CVPR 2017. Project webpage:
https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner
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
U4D: Unsupervised 4D Dynamic Scene Understanding
We introduce the first approach to solve the challenging problem of
unsupervised 4D visual scene understanding for complex dynamic scenes with
multiple interacting people from multi-view video. Our approach simultaneously
estimates a detailed model that includes a per-pixel semantically and
temporally coherent reconstruction, together with instance-level segmentation
exploiting photo-consistency, semantic and motion information. We further
leverage recent advances in 3D pose estimation to constrain the joint semantic
instance segmentation and 4D temporally coherent reconstruction. This enables
per person semantic instance segmentation of multiple interacting people in
complex dynamic scenes. Extensive evaluation of the joint visual scene
understanding framework against state-of-the-art methods on challenging indoor
and outdoor sequences demonstrates a significant (approx 40%) improvement in
semantic segmentation, reconstruction and scene flow accuracy.Comment: To appear in IEEE International Conference in Computer Vision ICCV
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