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。

基于稀疏点云的无监督占据学习

Unsupervised Occupancy Learning from Sparse Point Cloud

Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance
Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D
indoor scene reconstruction. We introduce a novel two-phase learning approach,
H2O-SDF, that discriminates between object and non-object regions within indoor
environments. This method achieves a nuanced balance, carefully preserving the
geometric integrity of room layouts while also capturing intricate surface
details of specific objects. A cornerstone of our two-phase learning framework
is the introduction of the Object Surface Field (OSF), a novel concept designed
to mitigate the persistent vanishing gradient problem that has previously
hindered the capture of high-frequency details in other methods. Our proposed
approach is validated through several experiments that include ablation
studies.

我们介绍了一种新颖的两阶段学习方法，H2O-SDF，用于区分室内环境中的对象和非对象区域。该方法在保持房间布局的几何完整性的同时，捕捉特定对象的复杂表面细节。我们的方法的核心是引入了一种名为对象表面场（OSF）的新概念，用于解决先前阻碍其他方法捕捉高频细节的持续消失梯度问题。通过多个实验，包括消融研究，验证了我们提出的方法。