Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A simple neural network is trained on the input planes to receive a 3D coordinate and return an inside/outside estimate for the query point. This prior is powerful in inducing smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing focusing on high frequencies at later stages. In addition, we identify and analyze a common ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries, cutting the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies.
翻译:重塑平面截面的3D形状是一个由医疗成像和地理信息学等下游应用引发的挑战。 输入是一个完全定义空间内飞机数量少的指标函数, 输出是整个数量指标函数的内插数。 先前为解决这一稀疏和错误的问题而开展的工作要么产生低质量结果, 要么依赖更多前科, 如目标表层、 外观信息或输入正常方向。 本文中, 我们提供 OREX, 3D 方法, 仅从切片形成重建, 以神经场为主, 以神经场为内插图, 之前的内插图为内插数。 一个简单的神经网络在输入平面上受到充分定义, 接受3D 协调, 并返回对查询点的内部和外部估计。 这在带来光滑和自定义上错误。 这个方法的主要挑战在于高频细节, 因为神经前是过于平滑动的。 为了缓解这一点, 我们提供了一种迭代估算架构和一个层次输入抽样的抽样抽样取样方法, 使得高频段能在较近的深度的深度的深度分析。 我们通过常规的深度的深度的深度的深度分析, 我们通过一个常规的深度分析, 分析, 通过常规的深度分析, 分析, 来显示和深度的深度的深度的深度分析, 显示和深度的深度的深度分析。</s>