6 research outputs found
Fast and easy blind deblurring using an inverse filter and PROBE
PROBE (Progressive Removal of Blur Residual) is a recursive framework for
blind deblurring. Using the elementary modified inverse filter at its core,
PROBE's experimental performance meets or exceeds the state of the art, both
visually and quantitatively. Remarkably, PROBE lends itself to analysis that
reveals its convergence properties. PROBE is motivated by recent ideas on
progressive blind deblurring, but breaks away from previous research by its
simplicity, speed, performance and potential for analysis. PROBE is neither a
functional minimization approach, nor an open-loop sequential method (blur
kernel estimation followed by non-blind deblurring). PROBE is a feedback
scheme, deriving its unique strength from the closed-loop architecture rather
than from the accuracy of its algorithmic components
Learning Gradient Fields for Shape Generation
In this work, we propose a novel technique to generate shapes from point
cloud data. A point cloud can be viewed as samples from a distribution of 3D
points whose density is concentrated near the surface of the shape. Point cloud
generation thus amounts to moving randomly sampled points to high-density
areas. We generate point clouds by performing stochastic gradient ascent on an
unnormalized probability density, thereby moving sampled points toward the
high-likelihood regions. Our model directly predicts the gradient of the log
density field and can be trained with a simple objective adapted from
score-based generative models. We show that our method can reach
state-of-the-art performance for point cloud auto-encoding and generation,
while also allowing for extraction of a high-quality implicit surface. Code is
available at https://github.com/RuojinCai/ShapeGF.Comment: Published in ECCV 2020 (Spotlight); Project page:
https://www.cs.cornell.edu/~ruojin/ShapeGF
Z2P: Instant Visualization of Point Clouds
We present a technique for visualizing point clouds using a neural network. Our technique allows for an instant preview of any point cloud, and bypasses the notoriously difficult surface reconstruction problem or the need to estimate oriented normals for splat-based rendering. We cast the preview problem as a conditional image-to-image translation task, and design a neural network that translates point depth-map directly into an image, where the point cloud is visualized as though a surface was reconstructed from it. Furthermore, the resulting appearance of the visualized point cloud can be, optionally, conditioned on simple control variables (e.g., color and light). We demonstrate that our technique instantly produces plausible images, and can, on-the-fly effectively handle noise, non-uniform sampling, and thin surfaces sheets
Geometric deep learning for post-menstrual age prediction based on the neonatal white matter cortical surface
Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week