Oblique-MERF: Revisiting and Improving MERF for Oblique Photography
This work addresses challenges in oblique aerial photography reconstruction for real-time rendering applications, representing an incremental improvement over existing methods.
The paper tackles the problem of reconstructing 3D scenes from oblique aerial photography, which suffers from high memory consumption and reduced rendering quality, by enhancing MERF with an adaptive occupancy plane and smoothness regularization, resulting in a 0.7 dB improvement over state-of-the-art methods, 40% VRAM reduction, and higher frame rates.
Neural implicit fields have established a new paradigm for scene representation, with subsequent work achieving high-quality real-time rendering. However, reconstructing 3D 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. In this paper, we enhance MERF to accommodate these data characteristics by introducing an innovative adaptive occupancy plane optimized during the volume rendering process and a smoothness regularization term for view-dependent color to address these issues. Our approach, termed Oblique-MERF, surpasses state-of-the-art real-time methods by approximately 0.7 dB, reduces VRAM usage by about 40%, and achieves higher rendering frame rates with more realistic rendering outcomes across most viewpoints.