  Neural Radiance Fields (NeRF) enable 3D scene reconstruction from 2D images
and camera poses for Novel View Synthesis (NVS). Although NeRF can produce
photorealistic results, it often suffers from overfitting to training views,
leading to poor geometry reconstruction, especially in low-texture areas. This
limitation restricts many important applications which require accurate
geometry, such as extrapolated NVS, HD mapping and scene editing. To address
this limitation, we propose a new method to improve NeRF's 3D structure using
only RGB images and semantic maps. Our approach introduces a novel plane
regularization based on Singular Value Decomposition (SVD), that does not rely
on any geometric prior. In addition, we leverage the Structural Similarity
Index Measure (SSIM) in our loss design to properly initialize the volumetric
representation of NeRF. Quantitative and qualitative results show that our
method outperforms popular regularization approaches in accurate geometry
reconstruction for large-scale outdoor scenes and achieves SoTA rendering
quality on the KITTI-360 NVS benchmark.
