1,955 research outputs found
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
We propose semantic region-adaptive normalization (SEAN), a simple but
effective building block for Generative Adversarial Networks conditioned on
segmentation masks that describe the semantic regions in the desired output
image. Using SEAN normalization, we can build a network architecture that can
control the style of each semantic region individually, e.g., we can specify
one style reference image per region. SEAN is better suited to encode,
transfer, and synthesize style than the best previous method in terms of
reconstruction quality, variability, and visual quality. We evaluate SEAN on
multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than
the current state of the art. SEAN also pushes the frontier of interactive
image editing. We can interactively edit images by changing segmentation masks
or the style for any given region. We can also interpolate styles from two
reference images per region.Comment: Accepted as a CVPR 2020 oral paper. The interactive demo is available
at https://youtu.be/0Vbj9xFgoU
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning
Ultrasound imaging makes use of backscattering of waves during their
interaction with scatterers present in biological tissues. Simulation of
synthetic ultrasound images is a challenging problem on account of inability to
accurately model various factors of which some include intra-/inter scanline
interference, transducer to surface coupling, artifacts on transducer elements,
inhomogeneous shadowing and nonlinear attenuation. Current approaches typically
solve wave space equations making them computationally expensive and slow to
operate. We propose a generative adversarial network (GAN) inspired approach
for fast simulation of patho-realistic ultrasound images. We apply the
framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation
performed using pseudo B-mode ultrasound image simulator yields speckle mapping
of a digitally defined phantom. The stage I GAN subsequently refines them to
preserve tissue specific speckle intensities. The stage II GAN further refines
them to generate high resolution images with patho-realistic speckle profiles.
We evaluate patho-realism of simulated images with a visual Turing test
indicating an equivocal confusion in discriminating simulated from real. We
also quantify the shift in tissue specific intensity distributions of the real
and simulated images to prove their similarity.Comment: To appear in the Proceedings of the 2018 IEEE International Symposium
on Biomedical Imaging (ISBI 2018
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