7,258 research outputs found
From 3D Models to 3D Prints: an Overview of the Processing Pipeline
Due to the wide diffusion of 3D printing technologies, geometric algorithms
for Additive Manufacturing are being invented at an impressive speed. Each
single step, in particular along the Process Planning pipeline, can now count
on dozens of methods that prepare the 3D model for fabrication, while analysing
and optimizing geometry and machine instructions for various objectives. This
report provides a classification of this huge state of the art, and elicits the
relation between each single algorithm and a list of desirable objectives
during Process Planning. The objectives themselves are listed and discussed,
along with possible needs for tradeoffs. Additive Manufacturing technologies
are broadly categorized to explicitly relate classes of devices and supported
features. Finally, this report offers an analysis of the state of the art while
discussing open and challenging problems from both an academic and an
industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and
Innovation action; Grant agreement N. 68044
Full Resolution Image Compression with Recurrent Neural Networks
This paper presents a set of full-resolution lossy image compression methods
based on neural networks. Each of the architectures we describe can provide
variable compression rates during deployment without requiring retraining of
the network: each network need only be trained once. All of our architectures
consist of a recurrent neural network (RNN)-based encoder and decoder, a
binarizer, and a neural network for entropy coding. We compare RNN types (LSTM,
associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study
"one-shot" versus additive reconstruction architectures and introduce a new
scaled-additive framework. We compare to previous work, showing improvements of
4.3%-8.8% AUC (area under the rate-distortion curve), depending on the
perceptual metric used. As far as we know, this is the first neural network
architecture that is able to outperform JPEG at image compression across most
bitrates on the rate-distortion curve on the Kodak dataset images, with and
without the aid of entropy coding.Comment: Updated with content for CVPR and removed supplemental material to an
external link for size limitation
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