VoxNeRF: ボクセル表現とNeRFを融合した屋内ビュー合成の高精度化
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
ボクセル符号化された幾何事前情報を活用し、効率的なサンプリングと深度損失で屋内のNeRF再構成と新規視点合成を高速・高品質化する手法を提案。
著者: Sen Wang, Qing Cheng, Stefano Gasperini, Wei Zhang, Shun-Cheng Wu, Niclas Zeller, Daniel Cremers, Nassir Navab
分類: cs.CV, cs.RO
原文アブストラクト
The generation of high-fidelity view synthesis is essential for robotic navigation and interaction but remains challenging, particularly in indoor environments and real-time scenarios. Existing techniques often require significant computational resources for both training and rendering, and they frequently result in suboptimal 3D representations due to insufficient geometric structuring. To address these limitations, we introduce VoxNeRF, a novel approach that utilizes easy-to-obtain geometry priors to enhance both the quality and efficiency of neural indoor reconstruction and novel view synthesis. We propose an efficient voxel-guided sampling technique that allocates computational resources selectively to the most relevant segments of rays based on a voxel-encoded geometry prior, significantly reducing training and rendering time. Additionally, we incorporate a robust depth loss to improve reconstruction and rendering quality in sparse view settings. Our approach is validated with extensive experiments on ScanNet and ScanNet++ where VoxNeRF outperforms existing state-of-the-art methods and establishes a new benchmark for indoor immersive interpolation and extrapolation settings.
