Zeng, Xiaoyi, Song, Kaiwen, Yang, Leyuan, Deng, Bailin ![]() ![]() |
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Abstract
Neural radiance fields (NeRF) have established a new paradigm for 3D scene reconstruction, with subsequent work achieving high-quality real-time rendering. However, reconstructing large-scale scenes from oblique aerial photography presents unique challenges, such as varying spatial scale distributions and a constrained range of tilt angles, often resulting in high memory consumption and reduced rendering quality at extrapolated viewpoints. To address these issues, we propose a novel approach named Oblique-MERF to accommodate the distinctive characteristics of oblique photography datasets and support real-time rendering on various common devices. Firstly, an innovative adaptive occupancy plane is proposed to constrain the sampling space. Additionally, we propose a smoothness regularization loss for view-dependent color to enhance the MLP's ability to generalize to untrained viewpoints. Experimental results demonstrate that Oblique-MERF reduces VRAM usage by approximately $40\%$ while maintaining competitive rendering quality compared to baseline methods, and achieves higher frame rates with more realistic rendering even at untrained extrapolated viewpoints. Project page: https://ustc3dv.github.io/Oblique-MERF/
Item Type: | Conference or Workshop Item (Paper) |
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Status: | In Press |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
Date of First Compliant Deposit: | 4 January 2025 |
Date of Acceptance: | 6 November 2024 |
Last Modified: | 17 Jan 2025 11:15 |
URI: | https://orca.cardiff.ac.uk/id/eprint/174989 |
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