24,888 research outputs found
Photometric Depth Super-Resolution
This study explores the use of photometric techniques (shape-from-shading and
uncalibrated photometric stereo) for upsampling the low-resolution depth map
from an RGB-D sensor to the higher resolution of the companion RGB image. A
single-shot variational approach is first put forward, which is effective as
long as the target's reflectance is piecewise-constant. It is then shown that
this dependency upon a specific reflectance model can be relaxed by focusing on
a specific class of objects (e.g., faces), and delegate reflectance estimation
to a deep neural network. A multi-shot strategy based on randomly varying
lighting conditions is eventually discussed. It requires no training or prior
on the reflectance, yet this comes at the price of a dedicated acquisition
setup. Both quantitative and qualitative evaluations illustrate the
effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(T-PAMI), 2019. First three authors contribute equall
Learning single-image 3D reconstruction by generative modelling of shape, pose and shading
We present a unified framework tackling two problems: class-specific 3D
reconstruction from a single image, and generation of new 3D shape samples.
These tasks have received considerable attention recently; however, most
existing approaches rely on 3D supervision, annotation of 2D images with
keypoints or poses, and/or training with multiple views of each object
instance. Our framework is very general: it can be trained in similar settings
to existing approaches, while also supporting weaker supervision. Importantly,
it can be trained purely from 2D images, without pose annotations, and with
only a single view per instance. We employ meshes as an output representation,
instead of voxels used in most prior work. This allows us to reason over
lighting parameters and exploit shading information during training, which
previous 2D-supervised methods cannot. Thus, our method can learn to generate
and reconstruct concave object classes. We evaluate our approach in various
settings, showing that: (i) it learns to disentangle shape from pose and
lighting; (ii) using shading in the loss improves performance compared to just
silhouettes; (iii) when using a standard single white light, our model
outperforms state-of-the-art 2D-supervised methods, both with and without pose
supervision, thanks to exploiting shading cues; (iv) performance improves
further when using multiple coloured lights, even approaching that of
state-of-the-art 3D-supervised methods; (v) shapes produced by our model
capture smooth surfaces and fine details better than voxel-based approaches;
and (vi) our approach supports concave classes such as bathtubs and sofas,
which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable
renderer available at https://github.com/pmh47/dir
Learning to Reconstruct Texture-less Deformable Surfaces from a Single View
Recent years have seen the development of mature solutions for reconstructing
deformable surfaces from a single image, provided that they are relatively
well-textured. By contrast, recovering the 3D shape of texture-less surfaces
remains an open problem, and essentially relates to Shape-from-Shading. In this
paper, we introduce a data-driven approach to this problem. We introduce a
general framework that can predict diverse 3D representations, such as meshes,
normals, and depth maps. Our experiments show that meshes are ill-suited to
handle texture-less 3D reconstruction in our context. Furthermore, we
demonstrate that our approach generalizes well to unseen objects, and that it
yields higher-quality reconstructions than a state-of-the-art SfS technique,
particularly in terms of normal estimates. Our reconstructions accurately model
the fine details of the surfaces, such as the creases of a T-Shirt worn by a
person.Comment: Accepted to 3DV 201
3D Face Reconstruction by Learning from Synthetic Data
Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.Comment: The first two authors contributed equally to this wor
SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild
We present SfSNet, an end-to-end learning framework for producing an accurate
decomposition of an unconstrained human face image into shape, reflectance and
illuminance. SfSNet is designed to reflect a physical lambertian rendering
model. SfSNet learns from a mixture of labeled synthetic and unlabeled real
world images. This allows the network to capture low frequency variations from
synthetic and high frequency details from real images through the photometric
reconstruction loss. SfSNet consists of a new decomposition architecture with
residual blocks that learns a complete separation of albedo and normal. This is
used along with the original image to predict lighting. SfSNet produces
significantly better quantitative and qualitative results than state-of-the-art
methods for inverse rendering and independent normal and illumination
estimation.Comment: Accepted to CVPR 2018 (Spotlight
Shape from Shading through Shape Evolution
In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark
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