Surface reconstruction from point clouds is a fundamental problem in the
computer vision and graphics community. Recent state-of-the-arts solve this
problem by individually optimizing each local implicit field during inference.
Without considering the geometric relationships between local fields, they
typically require accurate normals to avoid the sign conflict problem in
overlapped regions of local fields, which severely limits their applicability
to raw scans where surface normals could be unavailable. Although SAL breaks
this limitation via sign-agnostic learning, further works still need to explore
how to extend this technique for local shape modeling. To this end, we propose
to learn implicit surface reconstruction by sign-agnostic optimization of
convolutional occupancy networks, to simultaneously achieve advanced
scalability to large-scale scenes, generality to novel shapes, and
applicability to raw scans in a unified framework. Concretely, we achieve this
goal by a simple yet effective design, which further optimizes the pre-trained
occupancy prediction networks with an unsigned cross-entropy loss during
inference. The learning of occupancy fields is conditioned on convolutional
features from an hourglass network architecture. Extensive experimental
comparisons with previous state-of-the-arts on both object-level and
scene-level datasets demonstrate the superior accuracy of our approach for
surface reconstruction from un-orientated point clouds. The code is available
at this https URL

本研究提出一种使用无标记交叉熵损失的卷积占据网络进行表面重建的方法，以实现大规模场景的高级可扩展性、新型形状的一般性和原始扫描的适用性。实验结果表明，该方法对于未定向点云的表面重建具有优越的准确性。

SA-ConvONet：卷积占用网络的符号不可知优化

SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy  Networks

Recently, implicit neural representations have gained popularity for
learning-based 3D reconstruction. While demonstrating promising results, most
implicit approaches are limited to comparably simple geometry of single objects
and do not scale to more complicated or large-scale scenes. The key limiting
factor of implicit methods is their simple fully-connected network architecture
which does not allow for integrating local information in the observations or
incorporating inductive biases such as translational equivariance. In this
paper, we propose Convolutional Occupancy Networks, a more flexible implicit
representation for detailed reconstruction of objects and 3D scenes. By
combining convolutional encoders with implicit occupancy decoders, our model
incorporates inductive biases, enabling structured reasoning in 3D space. We
investigate the effectiveness of the proposed representation by reconstructing
complex geometry from noisy point clouds and low-resolution voxel
representations. We empirically find that our method enables the fine-grained
implicit 3D reconstruction of single objects, scales to large indoor scenes,
and generalizes well from synthetic to real data.

本文提出基于卷积的代表性网络 Convolutional Occupancy Networks 用于 3D 重建，实现了在较为复杂的几何情况下的结构化推理及其他先验偏差的融合，通过从噪声点云等数据中重建复杂几何模型且适用于大尺度室内场景。