VRGym

Xie, Xu; Liu, Hangxin; Zhang, Zhenliang; Qiu, Yuxing; Gao, Feng; Qi, Siyuan; Zhu, Yixin; Zhu, Song‐Chun

doi:10.1145/3321408.3322633

Cited by 23 publications

(1 citation statement)

References 21 publications

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“…Moreover, our system has potential applications beyond interactive scene reconstruction. It can be utilized to digitize real environments for virtual reality, creating in-situ simulations for robot planning and training [46,47], and facilitate the understanding of human-object interactions [48][49][50], among other downstream applications.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Scene Reconstruction with Functional Objects for Robot Autonomy

Han¹,

Zhang

Jiao

et al. 2022

Int J Comput Vis

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Existing methods for reconstructing interactive scenes primarily focus on replacing reconstructed objects with CAD models retrieved from a limited database, resulting in significant discrepancies between the reconstructed and observed scenes. To address this issue, our work introduces a partlevel reconstruction approach that reassembles objects using primitive shapes. This enables us to precisely replicate the observed physical scenes and simulate robot interactions with both rigid and articulated objects. By segmenting reconstructed objects into semantic parts and aligning primitive shapes to these parts, we assemble them as CAD models while estimating kinematic relations, including parent-child contact relations, joint types, and parameters. Specifically, we derive the optimal primitive alignment by solving a series of optimization problems, and estimate kinematic relations based on part semantics and geometry. Our experiments demonstrate that part-level scene reconstruction outperforms object-level reconstruction by accurately capturing finer details and improving precision. These reconstructed part-level interactive scenes provide valuable kinematic information for various robotic applications; we showcase the feasibility of certifying mobile manipulation planning in these interactive scenes before executing tasks in the physical world.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Scene Reconstruction with Functional Objects for Robot Autonomy

Han¹,

Zhang

Jiao

et al. 2022

Int J Comput Vis

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Synthetic Data for Deep Learning

Nikolenko

2021

Springer Optimization and Its Applications

222

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Aims and ScopeOptimization has continued to expand in all directions at an astonishing rate. New algorithmic and theoretical techniques are continually developing and the diffusion into other disciplines is proceeding at a rapid pace, with a spot light on machine learning, artificial intelligence, and quantum computing. Our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in areas not limited to applied mathematics, engineering, medicine, economics, computer science, operations research, and other sciences.The series Springer Optimization and Its Applications (SOIA) aims to publish state-of-the-art expository works (monographs, contributed volumes, textbooks, handbooks) that focus on theory, methods, and applications of optimization. Topics covered include, but are not limited to, nonlinear optimization, combinatorial optimization, continuous optimization, stochastic optimization, Bayesian optimization, optimal control, discrete optimization, multi-objective optimization, and more. New to the series portfolio include Works at the intersection of optimization and machine learning, artificial intelligence, and quantum computing.Volumes from this series are indexed by Web of Science, zbMATH, Mathematical Reviews, and SCOPUS.

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