DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer-Reference-Cited by-同舟云学术

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DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer

Published:2024-04 Issue:4 Volume:9 Page:3291-3298
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Wu Yizhe¹^ORCID,Borde Haitz Sáez de Ocáriz¹^ORCID,Collins Jack¹^ORCID,Jones Oiwi Parker¹^ORCID,Posner Ingmar¹^ORCID

Affiliation:

1. Applied AI Lab, Oxford Robotics Institute, Oxford, U.K.

Funder

EPSRC Programme

Amazon Research Award

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7083369/10440130/10423129.pdf?arnumber=10423129

Reference41 articles.

1. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

2. NeRF: Neural radiance field in 3D vision, a comprehensive review;Gao,2022

3. Evo-NeRF: Evolving NeRF for sequential robot grasping of transparent objects;Kerr,2023

4. NeRF-Loc: Visual Localization with Conditional Neural Radiance Field

5. Touching a NeRF: Leveraging neural radiance fields for tactile sensory data generation;Zhong,2023

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