46,012 research outputs found
Interactive inspection of complex multi-object industrial assemblies
The final publication is available at Springer via http://dx.doi.org/10.1016/j.cad.2016.06.005The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution–based on the analysis of several existing view-dependent visualization schemes–uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.Peer ReviewedPostprint (author's final draft
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
Early evaluation of Unistats: user experiences
This paper sets out the findings of the user evaluation of Unistats.UK Higher Education Funding Bodie
Neural Rays for Occlusion-aware Image-based Rendering
We present a new neural representation, called Neural Ray (NeuRay), for the
novel view synthesis task. Recent works construct radiance fields from image
features of input views to render novel view images, which enables the
generalization to new scenes. However, due to occlusions, a 3D point may be
invisible to some input views. On such a 3D point, these generalization methods
will include inconsistent image features from invisible views, which interfere
with the radiance field construction. To solve this problem, we predict the
visibility of 3D points to input views within our NeuRay representation. This
visibility enables the radiance field construction to focus on visible image
features, which significantly improves its rendering quality. Meanwhile, a
novel consistency loss is proposed to refine the visibility in NeuRay when
finetuning on a specific scene. Experiments demonstrate that our approach
achieves state-of-the-art performance on the novel view synthesis task when
generalizing to unseen scenes and outperforms per-scene optimization methods
after finetuning.Comment: CVPR2022. Project page https://liuyuan-pal.github.io/NeuRay/ Codes
available at https://github.com/liuyuan-pal/NeuRa
Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
As many different 3D volumes could produce the same 2D x-ray image, inverting
this process is challenging. We show that recent deep learning-based
convolutional neural networks can solve this task. As the main challenge in
learning is the sheer amount of data created when extending the 2D image into a
3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which
is then fused in a second step with the input x-ray into a high-resolution
volume. To train and validate our approach we introduce a new dataset that
comprises of close to half a million computer-simulated 2D x-ray images of 3D
volumes scanned from 175 mammalian species. Applications of our approach
include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays
including changes of illumination, view pose or geometry. Our evaluation
includes comparison to previous tomography work, previous learning methods
using our data, a user study and application to a set of real x-rays
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