309 research outputs found
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
Raw point clouds data inevitably contains outliers or noise through
acquisition from 3D sensors or reconstruction algorithms. In this paper, we
present a novel end-to-end network for robust point clouds processing, named
PointASNL, which can deal with point clouds with noise effectively. The key
component in our approach is the adaptive sampling (AS) module. It first
re-weights the neighbors around the initial sampled points from farthest point
sampling (FPS), and then adaptively adjusts the sampled points beyond the
entire point cloud. Our AS module can not only benefit the feature learning of
point clouds, but also ease the biased effect of outliers. To further capture
the neighbor and long-range dependencies of the sampled point, we proposed a
local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL
module enables the learning process insensitive to noise. Extensive experiments
verify the robustness and superiority of our approach in point clouds
processing tasks regardless of synthesis data, indoor data, and outdoor data
with or without noise. Specifically, PointASNL achieves state-of-the-art robust
performance for classification and segmentation tasks on all datasets, and
significantly outperforms previous methods on real-world outdoor SemanticKITTI
dataset with considerate noise. Our code is released through
https://github.com/yanx27/PointASNL.Comment: To appear in CVPR 2020. Also seen in
http://kaldir.vc.in.tum.de/scannet_benchmark
Sparse graph regularized mesh color edit propagation
Mesh color edit propagation aims to propagate the color from a few color strokes to the whole mesh, which is useful for mesh colorization, color enhancement and color editing, etc. Compared with image edit propagation, luminance information is not available for 3D mesh data, so the color edit propagation is more difficult on 3D meshes than images, with far less research carried out. This paper proposes a novel solution based on sparse graph regularization. Firstly, a few color strokes are interactively drawn by the user, and then the color will be propagated to the whole mesh by minimizing a sparse graph regularized nonlinear energy function. The proposed method effectively measures geometric similarity over shapes by using a set of complementary multiscale feature descriptors, and effectively controls color bleeding via a sparse
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optimization rather than quadratic minimization used in existing work. The proposed framework can be applied for the task of interactive mesh colorization, mesh color enhancement and mesh color editing. Extensive qualitative and quantitative experiments show that the proposed method outperforms the state-of-the-art methods
Graph-to-local limit for the nonlocal interaction equation
We study a class of nonlocal partial differential equations presenting a
tensor-mobility, in space, obtained asymptotically from nonlocal dynamics on
localising infinite graphs. Our strategy relies on the variational structure of
both equations, being a Riemannian and Finslerian gradient flow, respectively.
More precisely, we prove that weak solutions of the nonlocal interaction
equation on graphs converge to weak solutions of the aforementioned class of
nonlocal interaction equation with a tensor-mobility in the Euclidean space.
This highlights an interesting property of the graph, being a potential
space-discretisation for the equation under study.Comment: 48 pages. Comments welcom
PCT: Point cloud transformer
The irregular domain and lack of ordering make it challenging to design deep
neural networks for point cloud processing. This paper presents a novel
framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is
based on Transformer, which achieves huge success in natural language
processing and displays great potential in image processing. It is inherently
permutation invariant for processing a sequence of points, making it
well-suited for point cloud learning. To better capture local context within
the point cloud, we enhance input embedding with the support of farthest point
sampling and nearest neighbor search. Extensive experiments demonstrate that
the PCT achieves the state-of-the-art performance on shape classification, part
segmentation and normal estimation tasks.Comment: 11 pages, 5 figure
Nonlocal Multiscale Hierarchical Decomposition on Graphs
International audienceThe decomposition of images into their meaningful components is one of the major tasks in computer vision. Tadmor, Nezzar and Vese [1] have proposed a general approach for multiscale hierarchical decomposition of images. On the basis of this work, we propose a multiscale hierarchical decomposition of functions on graphs. The decomposition is based on a discrete variational framework that makes it possible to process arbitrary discrete data sets with the natural introduction of nonlocal interactions. This leads to an approach that can be used for the decomposition of images, meshes, or arbitrary data sets by taking advantage of the graph structure. To have a fully automatic decomposition, the issue of parameter selection is fully addressed. We illustrate our approach with numerous decomposition results on images, meshes, and point clouds and show the benefits
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