论文标题
神经泊松：神经场的指标功能
Neural Poisson: Indicator Functions for Neural Fields
论文作者
论文摘要
3D形状的隐式神经场生成签名的距离场表示（SDF）在3D形状的重建和生成中表现出了显着的进展。我们引入了一个新的范式，用于3D场景的神经田间表现形式；我们没有将表面表现为SDF，而是提出了对表面的泊松启发的表征，作为由神经场优化的指标函数。至关重要的是，对于重建真实扫描数据，指示器函数表示可以基于共同范围传感输入来实现简单有效的约束，该约束基于视线指示空白空间。这种空的空间信息是扫描过程的固有的，并结合了此知识，可以更准确地进行表面重建。我们表明，我们的方法证明了合成和实际扫描3D场景数据的最新重建性能，倒角距离比最新状态提高了9.5％。
Implicit neural field generating signed distance field representations (SDFs) of 3D shapes have shown remarkable progress in 3D shape reconstruction and generation. We introduce a new paradigm for neural field representations of 3D scenes; rather than characterizing surfaces as SDFs, we propose a Poisson-inspired characterization for surfaces as indicator functions optimized by neural fields. Crucially, for reconstruction of real scan data, the indicator function representation enables simple and effective constraints based on common range sensing inputs, which indicate empty space based on line of sight. Such empty space information is intrinsic to the scanning process, and incorporating this knowledge enables more accurate surface reconstruction. We show that our approach demonstrates state-of-the-art reconstruction performance on both synthetic and real scanned 3D scene data, with 9.5% improvement in Chamfer distance over state of the art.
