Geometry-Aware Scene Configurations for Novel View Synthesis

Technical Summary: Geometry-Aware Scene Configurations for Novel View Synthesis

Problem Statement
Recent advances in Neural Radiance Fields (NeRF) enable immersive 3D experiences from image observations, yet they face significant challenges when applied to large-scale indoor scenes captured via casual, hand-held trajectories. These scenarios often present irregular observation patterns, sparse data, and complex layouts containing clutter, occlusion, and flat, homogeneous surfaces. Standard scalable scene representations typically allocate their limited representational capacity (bases, such as NeRF blocks or feature grids) using uniform strategies—either dividing space evenly or distributing bases along the camera trajectory. These approaches fail to account for the non-uniform nature of indoor geometry or the specific statistics of the input measurements, leading to inefficient resource usage and degraded rendering quality, particularly in extrapolation scenarios or textureless regions. Furthermore, existing methods often struggle with floating artifacts in under-constrained regions due to a lack of geometric consistency.

Methodology
The authors propose a systematic, scene-adaptive framework that leverages readily available geometric priors to optimize both the placement of representation bases and the training view configuration. The method consists of three primary stages:

1. Geometric Scaffold Extraction: The pipeline begins by extracting an approximate geometric scaffold from the input multi-view images. This can be achieved via implicit surface reconstruction methods (e.g., MonoSDF) or feed-forward depth predictors (e.g., MapAnything). This scaffold serves as a consistent estimate of the underlying scene surface.
2. Geometry-Aware Basis Placement Optimization: Instead of uniform distribution, the method optimizes the positions of a limited set of bases (NeRF blocks or neural light field probes). It first records observation statistics on the geometric scaffold by calculating "coverage weights" for surface points. These weights accumulate visibility information from all training cameras, weighted by distance and surface normal alignment. An energy function is then minimized to shift basis positions toward regions with high observation density and complex geometry, ensuring that the fixed capacity of the bases is allocated where it is most needed.
3. Scene-Adaptive Geometric Regularization: To address regions with insufficient measurements (e.g., flat walls or occluded areas), the method introduces scene-aware virtual viewpoints. Using a next-best-view planning strategy that considers spatial diversity and feature co-visibility, the system selects virtual viewpoints to cover the scene. Depths rendered from these virtual views, guided by the geometric scaffold, are used to impose robust depth regularization during training. This suppresses floating artifacts and stabilizes optimization in sparsely observed regions.

Key Contributions

• Scene-Adaptive Basis Distribution: The authors introduce an optimization strategy that places bases based on the correlation between scene geometry and measurement statistics, outperforming uniform or trajectory-based allocation methods.
• Energy-Based Placement Optimization: A novel energy function is defined on the geometric scaffold to accumulate observation statistics, allowing for the optimal positioning of a limited set of bases to cover the input trajectory efficiently.
• Virtual Viewpoint Regularization: The paper suggests a method to generate and utilize scene-adaptive virtual viewpoints to provide plausible supervision in unobserved regions, effectively regularizing the reconstruction without requiring additional real-world captures.
• Comprehensive Evaluation: The framework is validated on real-world indoor scenes from the ScanNet++ and Zip-NeRF datasets, demonstrating significant improvements over baselines like NeLF-Pro, LocalRF, and various 3D Gaussian Splatting (3DGS) variants.

Results
Experimental results on ScanNet++ and Zip-NeRF datasets demonstrate that the proposed method significantly enhances reconstruction quality in terms of PSNR, SSIM, and LPIPS, particularly in challenging extrapolation scenarios where baselines often fail.

• Performance: When applied to NeLF-Pro and LocalRF, the method achieves state-of-the-art performance among NeRF-based approaches, often outperforming 3DGS variants in extrapolation tasks where 3DGS suffers from severe artifacts.
• Efficiency: The approach achieves these gains without increasing memory usage compared to the underlying baseline architectures, as it reuses the same number of parameters but optimizes their spatial arrangement.
• Robustness: Ablation studies confirm that both the basis placement optimization and the geometric regularization (using virtual views) contribute independently to performance. The method also shows robustness to inaccuracies in the geometric scaffold, maintaining improvements even when the prior geometry is noisy.

Significance and Claims
The paper claims that by explicitly modeling the interplay between underlying geometry and pre-captured camera viewpoints, it is possible to maximize the utilization of limited resources for large-scale indoor scene reconstruction. The authors emphasize that while 3D Gaussian Splatting offers speed, NeRF-based representations retain advantages in memory efficiency and geometric consistency for large-scale, unbounded scenes, particularly in textureless regions. The proposed scene-adaptive strategies are presented as a generalizable enhancement applicable to various novel-view synthesis representations, offering a principled solution to the non-trivial challenge of allocating representational capacity in complex indoor environments. The work does not claim to solve catastrophic failures in upstream SfM or calibration but focuses on improving reconstruction fidelity given a successful geometric scaffold.