Implicit Neural Representations have gained prominence as a powerful
framework for capturing complex data modalities, encompassing a wide range from
3D shapes to images and audio. Within the realm of 3D shape representation,
Neural Signed Distance Functions (SDF) have demonstrated remarkable potential
in faithfully encoding intricate shape geometry. However, learning SDFs from 3D
point clouds in the absence of ground truth supervision remains a very
challenging task. In this paper, we propose a method to infer occupancy fields
instead of SDFs as they are easier to learn from sparse inputs. We leverage a
margin-based uncertainty measure to differentially sample from the decision
boundary of the occupancy function and supervise the sampled boundary points
using the input point cloud. We further stabilize the optimization process at
the early stages of the training by biasing the occupancy function towards
minimal entropy fields while maximizing its entropy at the input point cloud.
Through extensive experiments and evaluations, we illustrate the efficacy of
our proposed method, highlighting its capacity to improve implicit shape
inference with respect to baselines and the state-of-the-art using synthetic
and real data.

通过使用基于边界采样的方法和基于熵的优化过程，我们提出一种从稀疏输入中学习占据场的方法，并展示了该方法在隐式形状推断方面相对于基线方法和现有技术的有效性。