30,481 research outputs found
Automatic Objects Removal for Scene Completion
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as 3D scene reconstruction and
other big data applications. However, this is not an easy task due to the fact
the retrieved photos are neither aligned nor calibrated. Furthermore, with the
occlusion of unexpected foreground objects like people, vehicles, it is even
more challenging to find feature correspondences and reconstruct realistic
scenes. In this paper, we propose a structure based image completion algorithm
for object removal that produces visually plausible content with consistent
structure and scene texture. We use an edge matching technique to infer the
potential structure of the unknown region. Driven by the estimated structure,
texture synthesis is performed automatically along the estimated curves. We
evaluate the proposed method on different types of images: from highly
structured indoor environment to the natural scenes. Our experimental results
demonstrate satisfactory performance that can be potentially used for
subsequent big data processing: 3D scene reconstruction and location
recognition.Comment: 6 pages, IEEE International Conference on Computer Communications
(INFOCOM 14), Workshop on Security and Privacy in Big Data, Toronto, Canada,
201
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
MVF-Net: Multi-View 3D Face Morphable Model Regression
We address the problem of recovering the 3D geometry of a human face from a
set of facial images in multiple views. While recent studies have shown
impressive progress in 3D Morphable Model (3DMM) based facial reconstruction,
the settings are mostly restricted to a single view. There is an inherent
drawback in the single-view setting: the lack of reliable 3D constraints can
cause unresolvable ambiguities. We in this paper explore 3DMM-based shape
recovery in a different setting, where a set of multi-view facial images are
given as input. A novel approach is proposed to regress 3DMM parameters from
multi-view inputs with an end-to-end trainable Convolutional Neural Network
(CNN). Multiview geometric constraints are incorporated into the network by
establishing dense correspondences between different views leveraging a novel
self-supervised view alignment loss. The main ingredient of the view alignment
loss is a differentiable dense optical flow estimator that can backpropagate
the alignment errors between an input view and a synthetic rendering from
another input view, which is projected to the target view through the 3D shape
to be inferred. Through minimizing the view alignment loss, better 3D shapes
can be recovered such that the synthetic projections from one view to another
can better align with the observed image. Extensive experiments demonstrate the
superiority of the proposed method over other 3DMM methods.Comment: 2019 Conference on Computer Vision and Pattern Recognitio
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