We introduce FastSurf, an accelerated neural radiance field (NeRF) framework that incorporates depth information for 3D reconstruction. A dense feature grid and shallow multi-layer perceptron are used for fast and accurate surface optimization of the entire scene. Our per-frame intrinsic refinement scheme corrects the frame-specific errors that cannot be handled by global optimization. Furthermore, FastSurf utilizes a classical real-time 3D surface reconstruction method, the truncated signed distance field (TSDF) Fusion, as prior knowledge to pretrain the feature grid to accelerate the training. The quantitative and qualitative experiments comparing the performances of FastSurf against prior work indicate that our method is capable of quickly and accurately reconstructing a scene with high-frequency details. We also demonstrate the effectiveness of our per-frame intrinsic refinement and TSDF Fusion prior learning techniques via an ablation study.

翻译：我们提出FastSurf，一种加速的神经辐射场（NeRF）框架，该框架融合深度信息用于三维重建。通过密集特征网格与浅层多层感知机，实现了对整个场景的快速且准确的表面优化。我们的逐帧内参优化方案能够校正全局优化无法处理的逐帧特定误差。此外，FastSurf利用经典实时三维表面重建方法——截断符号距离场（TSDF）融合作为先验知识，对特征网格进行预训练以加速训练过程。通过与现有方法的定量与定性实验对比，结果表明我们的方法能够快速准确地重建具有高频细节的场景。消融研究也证实了逐帧内参优化与TSDF融合先验学习技术的有效性。