11,334 research outputs found
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
3D reconstruction from a single image is a key problem in multiple
applications ranging from robotic manipulation to augmented reality. Prior
methods have tackled this problem through generative models which predict 3D
reconstructions as voxels or point clouds. However, these methods can be
computationally expensive and miss fine details. We introduce a new
differentiable layer for 3D data deformation and use it in DeformNet to learn a
model for 3D reconstruction-through-deformation. DeformNet takes an image
input, searches the nearest shape template from a database, and deforms the
template to match the query image. We evaluate our approach on the ShapeNet
dataset and show that - (a) the Free-Form Deformation layer is a powerful new
building block for Deep Learning models that manipulate 3D data (b) DeformNet
uses this FFD layer combined with shape retrieval for smooth and
detail-preserving 3D reconstruction of qualitatively plausible point clouds
with respect to a single query image (c) compared to other state-of-the-art 3D
reconstruction methods, DeformNet quantitatively matches or outperforms their
benchmarks by significant margins. For more information, visit:
https://deformnet-site.github.io/DeformNet-website/ .Comment: 11 pages, 9 figures, NIP
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
Persistence Bag-of-Words for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a
robust descriptor of data in the form of persistence diagrams (PDs). PDs
exhibit, however, complex structure and are difficult to integrate in today's
machine learning workflows. This paper introduces persistence bag-of-words: a
novel and stable vectorized representation of PDs that enables the seamless
integration with machine learning. Comprehensive experiments show that the new
representation achieves state-of-the-art performance and beyond in much less
time than alternative approaches.Comment: Accepted for the Twenty-Eight International Joint Conference on
Artificial Intelligence (IJCAI-19). arXiv admin note: substantial text
overlap with arXiv:1802.0485
SHREC'16: partial matching of deformable shapes
Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching problems to be solved for each method - making this benchmark the biggest and most challenging of its kind. Five matching algorithms were evaluated in the contest; this paper presents the details of the dataset, the adopted evaluation measures, and shows thorough comparisons among all competing methods
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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