Point clouds offer an attractive source of information to complement images
in neural scene representations, especially when few images are available.
Neural rendering methods based on point clouds do exist, but they do not
perform well when the point cloud quality is low -- e.g., sparse or incomplete,
which is often the case with real-world data. We overcome these problems with a
simple representation that aggregates point clouds at multiple scale levels
with sparse voxel grids at different resolutions. To deal with point cloud
sparsity, we average across multiple scale levels -- but only among those that
are valid, i.e., that have enough neighboring points in proximity to the ray of
a pixel. To help model areas without points, we add a global voxel at the
coarsest scale, thus unifying "classical" and point-based NeRF formulations. We
validate our method on the NeRF Synthetic, ScanNet, and KITTI-360 datasets,
outperforming the state of the art by a significant margin.

使用多尺度稀疏体素网格聚合点云，平均多个尺度以克服点云稀疏性，在全局尺度加入体素表示模拟无点区域，并在 NeRF 合成、ScanNet 和 KITTI-360 数据集上验证方法，显著优于现有技术。

PointNeRF++: 多尺度、基于点的神经辐射场

PointNeRF++: A multi-scale, point-based Neural Radiance Field

Pothole detection is crucial for road safety and maintenance, traditionally
relying on 2D image segmentation. However, existing 3D Semantic Pothole
Segmentation research often overlooks point cloud sparsity, leading to
suboptimal local feature capture and segmentation accuracy. Our research
presents an innovative point cloud-based pothole segmentation architecture. Our
model efficiently identifies hidden features and uses a feedback mechanism to
enhance local characteristics, improving feature presentation. We introduce a
local relationship learning module to understand local shape relationships,
enhancing structural insights. Additionally, we propose a lightweight adaptive
structure for refining local point features using the K nearest neighbor
algorithm, addressing point cloud density differences and domain selection.
Shared MLP Pooling is integrated to learn deep aggregation features,
facilitating semantic data exploration and segmentation guidance. Extensive
experiments on three public datasets confirm PotholeGuard's superior
performance over state-of-the-art methods. Our approach offers a promising
solution for robust and accurate 3D pothole segmentation, with applications in
road maintenance and safety.

我们的研究提出了一种创新的基于点云的坑洞分割架构（PotholeGuard），通过有效地识别隐藏的特征并利用反馈机制增强局部特征，提高特征表达。我们引入了本地关系学习模块以了解局部形状关系，增强结构洞察力。此外，我们提出了一个轻量级的自适应结构，使用 K 最近邻算法来细化局部点特征，解决点云密度差异和域选择问题。集成了共享 MLP 汇聚特征的方法，促进语义数据的探索和分割引导。在三个公共数据集上进行了大量实验，验证了 PotholeGuard 相比于现有先进方法的卓越性能。我们的方法为强大而准确的 3D 坑洞分割提供了有希望的解决方案，具有道路维护和安全应用的潜力。