2,643 research outputs found
Geometry meets semantics for semi-supervised monocular depth estimation
Depth estimation from a single image represents a very exciting challenge in
computer vision. While other image-based depth sensing techniques leverage on
the geometry between different viewpoints (e.g., stereo or structure from
motion), the lack of these cues within a single image renders ill-posed the
monocular depth estimation task. For inference, state-of-the-art
encoder-decoder architectures for monocular depth estimation rely on effective
feature representations learned at training time. For unsupervised training of
these models, geometry has been effectively exploited by suitable images
warping losses computed from views acquired by a stereo rig or a moving camera.
In this paper, we make a further step forward showing that learning semantic
information from images enables to improve effectively monocular depth
estimation as well. In particular, by leveraging on semantically labeled images
together with unsupervised signals gained by geometry through an image warping
loss, we propose a deep learning approach aimed at joint semantic segmentation
and depth estimation. Our overall learning framework is semi-supervised, as we
deploy groundtruth data only in the semantic domain. At training time, our
network learns a common feature representation for both tasks and a novel
cross-task loss function is proposed. The experimental findings show how,
jointly tackling depth prediction and semantic segmentation, allows to improve
depth estimation accuracy. In particular, on the KITTI dataset our network
outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Direct prediction of 3D body pose and shape remains a challenge even for
highly parameterized deep learning models. Mapping from the 2D image space to
the prediction space is difficult: perspective ambiguities make the loss
function noisy and training data is scarce. In this paper, we propose a novel
approach (Neural Body Fitting (NBF)). It integrates a statistical body model
within a CNN, leveraging reliable bottom-up semantic body part segmentation and
robust top-down body model constraints. NBF is fully differentiable and can be
trained using 2D and 3D annotations. In detailed experiments, we analyze how
the components of our model affect performance, especially the use of part
segmentations as an explicit intermediate representation, and present a robust,
efficiently trainable framework for 3D human pose estimation from 2D images
with competitive results on standard benchmarks. Code will be made available at
http://github.com/mohomran/neural_body_fittingComment: 3DV 201
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Learning to Reconstruct People in Clothing from a Single RGB Camera
We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach
Dynamic Body VSLAM with Semantic Constraints
Image based reconstruction of urban environments is a challenging problem
that deals with optimization of large number of variables, and has several
sources of errors like the presence of dynamic objects. Since most large scale
approaches make the assumption of observing static scenes, dynamic objects are
relegated to the noise modeling section of such systems. This is an approach of
convenience since the RANSAC based framework used to compute most multiview
geometric quantities for static scenes naturally confine dynamic objects to the
class of outlier measurements. However, reconstructing dynamic objects along
with the static environment helps us get a complete picture of an urban
environment. Such understanding can then be used for important robotic tasks
like path planning for autonomous navigation, obstacle tracking and avoidance,
and other areas. In this paper, we propose a system for robust SLAM that works
in both static and dynamic environments. To overcome the challenge of dynamic
objects in the scene, we propose a new model to incorporate semantic
constraints into the reconstruction algorithm. While some of these constraints
are based on multi-layered dense CRFs trained over appearance as well as motion
cues, other proposed constraints can be expressed as additional terms in the
bundle adjustment optimization process that does iterative refinement of 3D
structure and camera / object motion trajectories. We show results on the
challenging KITTI urban dataset for accuracy of motion segmentation and
reconstruction of the trajectory and shape of moving objects relative to ground
truth. We are able to show average relative error reduction by a significant
amount for moving object trajectory reconstruction relative to state-of-the-art
methods like VISO 2, as well as standard bundle adjustment algorithms
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