LiDAR semantic segmentation frameworks predominantly leverage geometry-based
features to differentiate objects within a scan. While these methods excel in
scenarios with clear boundaries and distinct shapes, their performance declines
in environments where boundaries are blurred, particularly in off-road
contexts. To address this, recent strides in 3D segmentation algorithms have
focused on harnessing raw LiDAR intensity measurements to improve prediction
accuracy. Despite these efforts, current learning-based models struggle to
correlate the intricate connections between raw intensity and factors such as
distance, incidence angle, material reflectivity, and atmospheric conditions.
Building upon our prior work, this paper delves into the advantages of
employing calibrated intensity (also referred to as reflectivity) within
learning-based LiDAR semantic segmentation frameworks. We initially establish
that incorporating reflectivity as an input enhances the existing LiDAR
semantic segmentation model. Furthermore, we present findings that enable the
model to learn to calibrate intensity can boost its performance. Through
extensive experimentation on the off-road dataset Rellis-3D, we demonstrate
notable improvements. Specifically, converting intensity to reflectivity
results in a 4% increase in mean Intersection over Union (mIoU) when compared
to using raw intensity in Off-road scenarios. Additionally, we also investigate
the possible benefits of using calibrated intensity in semantic segmentation in
urban environments (SemanticKITTI) and cross-sensor domain adaptation.

基于校准强度（也称为反射率）的学习型激光雷达语义分割框架在边界模糊环境中表现良好，并通过将强度转换为反射率，提高了离线场景中的平均交并比（mIoU）4％。

反射率就是您所需的一切！：推进激光雷达语义分割

Reflectivity Is All You Need!: Advancing LiDAR Semantic Segmentation

Storing and transmitting LiDAR point cloud data is essential for many AV
applications, such as training data collection, remote control, cloud services
or SLAM. However, due to the sparsity and unordered structure of the data, it
is difficult to compress point cloud data to a low volume. Transforming the raw
point cloud data into a dense 2D matrix structure is a promising way for
applying compression algorithms. We propose a new lossless and calibrated
3D-to-2D transformation which allows compression algorithms to efficiently
exploit spatial correlations within the 2D representation. To compress the
structured representation, we use common image compression methods and also a
self-supervised deep compression approach using a recurrent neural network. We
also rearrange the LiDAR's intensity measurements to a dense 2D representation
and propose a new metric to evaluate the compression performance of the
intensity. Compared to approaches that are based on generic octree point cloud
compression or based on raw point cloud data compression, our approach achieves
the best quantitative and visual performance. Source code and dataset are
available at this https URL

我们提出了一种新的无损校准的 3D 至 2D 转换方法，使压缩算法能够高效利用二维表示中的空间相关性，通过常见的图像压缩方法和利用递归神经网络的自监督深度压缩方法对结构化表示进行压缩，还对 LiDAR 的强度测量值进行密集二维表示，并提出了一种评估压缩性能的新指标。与基于通用八叉树点云压缩或基于原始点云数据压缩的方法相比，我们的方法在定量和视觉性能上取得了最佳效果。

基于循环神经网络和图像压缩方法的 3D 点云压缩

3D Point Cloud Compression with Recurrent Neural Network and Image  Compression Methods

A complex frame is a collection of vectors that span $\mathbb{C}^M$ and
define measurements, called intensity measurements, on vectors in
$\mathbb{C}^M$. In purely mathematical terms, the problem of phase retrieval is
to recover a complex vector from its intensity measurements, namely the modulus
of its inner product with these frame vectors. We show that any vector is
uniquely determined (up to a global phase factor) from $4M-4$ generic
measurements. To prove this, we identify the set of frames defining
non-injective measurements with the projection of a real variety and bound its
dimension.

该研究论文探究了利用复向量的强度测量来进行相位恢复，并证明了通过 4M-4 个泛化测量可以唯一地确定一个向量（最多相差全局相位因子）。