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.ioIOblique-MERFI

Various SDF-based neural implicit surface reconstruction methods have been proposed recently, and have demonstrated remarkable modeling capabilities. However, due to the global nature and limited representation ability of a single network, existing methods still suffer from many drawbacks, such as limited accuracy and scale of the reconstruction. In this paper, we propose a versatile, scalable and high-quality neural implicit representation to address these issues. We integrate a divide-and-conquer approach into the neural SDF-based reconstruction. Specifically, we model the object or scene as a fusion of multiple independent local neural SDFs with overlapping regions. The construction of our representation involves three key steps: (1) constructing the distribution and overlap relationship of the local radiance fields based on object structure or data distribution, (2) relative pose registration for adjacent local SDFs, and (3) SDF blending. Thanks to the independent representation of each local region, our approach can not only achieve high-fidelity surface reconstruction, but also enable scalable scene reconstruction. Extensive experimental results demonstrate the effectiveness and practicality of our proposed method.