[2108.03072] STR-GQN: Scene Representation And Rendering For ...

Computer Science > Computer Vision and Pattern Recognition arXiv:2108.03072 (cs) [Submitted on 6 Aug 2021] Title:STR-GQN: Scene Representation and Rendering for Unknown Cameras Based on Spatial Transformation Routing Authors:Wen-Cheng Chen, Min-Chun Hu, Chu-Song Chen View a PDF of the paper titled STR-GQN: Scene Representation and Rendering for Unknown Cameras Based on Spatial Transformation Routing, by Wen-Cheng Chen and 2 other authors View PDF

Abstract:Geometry-aware modules are widely applied in recent deep learning architectures for scene representation and rendering. However, these modules require intrinsic camera information that might not be obtained accurately. In this paper, we propose a Spatial Transformation Routing (STR) mechanism to model the spatial properties without applying any geometric prior. The STR mechanism treats the spatial transformation as the message passing process, and the relation between the view poses and the routing weights is modeled by an end-to-end trainable neural network. Besides, an Occupancy Concept Mapping (OCM) framework is proposed to provide explainable rationals for scene-fusion processes. We conducted experiments on several datasets and show that the proposed STR mechanism improves the performance of the Generative Query Network (GQN). The visualization results reveal that the routing process can pass the observed information from one location of some view to the associated location in the other view, which demonstrates the advantage of the proposed model in terms of spatial cognition.

Subjects:	Computer Vision and Pattern Recognition (cs.CV)
ACM classes:	I.4.8
Cite as:	arXiv:2108.03072 [cs.CV]
(or arXiv:2108.03072v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2108.03072 Focus to learn more arXiv-issued DOI via DataCite

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From: Wen-Cheng Chen [view email] [v1] Fri, 6 Aug 2021 12:10:22 UTC (8,300 KB) Full-text links:

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