FastSurf: Fast Neural RGB-D Surface Reconstruction using Per-Frame Intrinsic Refinement and TSDF Fusion Prior Learning
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.
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