论文标题
流道：快速，高保真RGB-D表面重建的神经特征网格优化
GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction
论文作者
论文摘要
我们提出了GO-SURF，这是一种直接的特征网格优化方法，可从RGB-D序列进行准确和快速的表面重建。我们用学习的层次特征素网格封装了多级几何和外观局部信息的基础场景对基础场景进行建模。直接优化了特征向量，以使三线性插值后，由两个浅MLP解码为签名的距离和辐射值，并通过表面体积渲染渲染，合成和观察到的RGB/depth值之间的差异最小化。我们的监督信号-RGB，深度和近似SDF可以直接从输入图像中获得，而无需融合或后处理。我们制定了一种新型的SDF梯度正则化项，该项鼓励表面平滑度和孔填充，同时保持高频细节。 Go-Surf可以优化$ 1 $ - $ 2 $ k框架的序列，价格为$ 15 $ - $ 45 $分钟，$ \ times60 $的速度超过了NeuralRGB-D，这是基于MLP表示的最相关方法，同时在标准基准测试上保持PAR性能。项目页面：https：//jingwenwang95.github.io/go_surf/
We present GO-Surf, a direct feature grid optimization method for accurate and fast surface reconstruction from RGB-D sequences. We model the underlying scene with a learned hierarchical feature voxel grid that encapsulates multi-level geometric and appearance local information. Feature vectors are directly optimized such that after being tri-linearly interpolated, decoded by two shallow MLPs into signed distance and radiance values, and rendered via surface volume rendering, the discrepancy between synthesized and observed RGB/depth values is minimized. Our supervision signals -- RGB, depth and approximate SDF -- can be obtained directly from input images without any need for fusion or post-processing. We formulate a novel SDF gradient regularization term that encourages surface smoothness and hole filling while maintaining high frequency details. GO-Surf can optimize sequences of $1$-$2$K frames in $15$-$45$ minutes, a speedup of $\times60$ over NeuralRGB-D, the most related approach based on an MLP representation, while maintaining on par performance on standard benchmarks. Project page: https://jingwenwang95.github.io/go_surf/
