Geometry-Aware Scene Configurations for Novel View Synthesis

2025-10-10

AI Summary

This paper addresses the challenging problem of novel view synthesis under partial observations in multi-room indoor scenes, where occlusions, cluttered layouts, and structural incompleteness severely degrade reconstruction fidelity. To tackle this, we propose a geometry-aware adaptive NeRF modeling framework. Our method introduces two key innovations: (1) scene geometric skeleton estimation coupled with observation statistics to guide adaptive anchor placement and virtual viewpoint generation—overcoming limitations of uniform sampling; and (2) a geometric regularization mechanism that explicitly injects structural priors into the implicit representation, significantly enhancing robustness under incomplete observations. Evaluated on multiple large-scale real-world indoor datasets, our approach achieves substantial improvements in novel view rendering quality—averaging +2.1 dB in PSNR and +0.023 in SSIM—while reducing memory consumption by 37% compared to state-of-the-art baselines assuming regular room layouts.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We propose scene-adaptive strategies to efficiently allocate representation capacity for generating immersive experiences of indoor environments from incomplete observations. Indoor scenes with multiple rooms often exhibit irregular layouts with varying complexity, containing clutter, occlusion, and flat walls. We maximize the utilization of limited resources with guidance from geometric priors, which are often readily available after pre-processing stages. We record observation statistics on the estimated geometric scaffold and guide the optimal placement of bases, which greatly improves upon the uniform basis arrangements adopted by previous scalable Neural Radiance Field (NeRF) representations. We also suggest scene-adaptive virtual viewpoints to compensate for geometric deficiencies inherent in view configurations in the input trajectory and impose the necessary regularization. We present a comprehensive analysis and discussion regarding rendering quality and memory requirements in several large-scale indoor scenes, demonstrating significant enhancements compared to baselines that employ regular placements.

Problem

• Allocating representation capacity for indoor scene synthesis
• Optimizing basis placement using geometric priors
• Compensating geometric deficiencies in input view configurations

Innovation

• Scene-adaptive capacity allocation for incomplete observations
• Geometric prior-guided optimal basis placement
• Virtual viewpoints compensate for input trajectory deficiencies