218,242 research outputs found
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
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
A Portable Active Binocular Robot Vision Architecture for Scene Exploration
We present a portable active binocular robot vision archi-
tecture that integrates a number of visual behaviours. This vision archi-
tecture inherits the abilities of vergence, localisation, recognition and si-
multaneous identification of multiple target object instances. To demon-
strate the portability of our vision architecture, we carry out qualitative
and comparative analysis under two different hardware robotic settings,
feature extraction techniques and viewpoints. Our portable active binoc-
ular robot vision architecture achieved average recognition rates of 93.5%
for fronto-parallel viewpoints and, 83% percentage for anthropomorphic
viewpoints, respectively
Unmasked Teacher: Towards Training-Efficient Video Foundation Models
Video Foundation Models (VFMs) have received limited exploration due to high
computational costs and data scarcity. Previous VFMs rely on Image Foundation
Models (IFMs), which face challenges in transferring to the video domain.
Although VideoMAE has trained a robust ViT from limited data, its low-level
reconstruction poses convergence difficulties and conflicts with high-level
cross-modal alignment. This paper proposes a training-efficient method for
temporal-sensitive VFMs that integrates the benefits of existing methods. To
increase data efficiency, we mask out most of the low-semantics video tokens,
but selectively align the unmasked tokens with IFM, which serves as the
UnMasked Teacher (UMT). By providing semantic guidance, our method enables
faster convergence and multimodal friendliness. With a progressive pre-training
framework, our model can handle various tasks including scene-related,
temporal-related, and complex video-language understanding. Using only public
sources for pre-training in 6 days on 32 A100 GPUs, our scratch-built ViT-L/16
achieves state-of-the-art performances on various video tasks. The code and
models will be released at https://github.com/OpenGVLab/unmasked_teacher.Comment: 16 pages, 5 figures, 28 table
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