Point clouds are vital in computer vision tasks such as 3D reconstruction,
autonomous driving, and robotics. However, TLS-acquired point clouds often
contain virtual points from reflective surfaces, causing disruptions. This
study presents a reflection noise elimination algorithm for TLS point clouds.
Our innovative reflection plane detection algorithm, based on geometry-optical
models and physical properties, identifies and categorizes reflection points
per optical reflection theory. We've adapted the LSFH feature descriptor to
retain reflection features, mitigating interference from symmetrical
architectural structures. By incorporating the Hausdorff feature distance, the
algorithm enhances resilience to ghosting and deformation, improving virtual
point detection accuracy. Extensive experiments on the 3DRN benchmark dataset,
featuring diverse urban environments with virtual TLS reflection noise, show
our algorithm improves precision and recall rates for 3D points in reflective
regions by 57.03\% and 31.80\%, respectively. Our method achieves a 9.17\%
better outlier detection rate and 5.65\% higher accuracy than leading methods.
Access the 3DRN dataset at (this https URL).

基于几何光学模型和物理性质的反射噪声消除算法，提高了 TLS 点云中的虚拟点检测精度，并通过在 3DRN 基准数据集上的实验证明了算法的有效性。

基于辐射校正的光学建模方法用于去除城市场景 TLS 点云中的反射噪声

A Radiometric Correction based Optical Modeling Approach to Removing  Reflection Noise in TLS Point Clouds of Urban Scenes

We propose a robust approach for the registration of two sets of 3D points in
the presence of a large amount of outliers. Our first contribution is to
reformulate the registration problem using a Truncated Least Squares (TLS) cost
that makes the estimation insensitive to a large fraction of spurious
point-to-point correspondences. The second contribution is a general framework
to decouple rotation, translation, and scale estimation, which allows solving
in cascade for the three transformations. Since each subproblem (scale,
rotation, and translation estimation) is still non-convex and combinatorial in
nature, out third contribution is to show that (i) TLS scale and
(component-wise) translation estimation can be solved exactly and in polynomial
time via an adaptive voting scheme, (ii) TLS rotation estimation can be relaxed
to a semidefinite program and the relaxation is tight in practice, even in the
presence of an extreme amount of outliers. We validate the proposed algorithm,
named TEASER (Truncated least squares Estimation And SEmidefinite Relaxation),
in standard registration benchmarks showing that the algorithm outperforms
RANSAC and robust local optimization techniques, and favorably compares with
Branch-and-Bound methods, while being a polynomial-time algorithm. TEASER can
tolerate up to 99% outliers and returns highly-accurate solutions.

在大量异常值存在的情况下，我们提出了一种用于 3D 点集配准的健壮方法，其中第一个贡献是使用截断最小二乘（TLS）成本重新制定了配准问题，使得估计对大量虚假对应不敏感；第二个贡献是解耦旋转、平移和缩放估计的通用框架，允许按级联顺序解决三个转换。

极端异常值率下的稳健配准多项式时间解决方案

A Polynomial-time Solution for Robust Registration with Extreme Outlier  Rates

The use of TLS by malware poses new challenges to network threat detection
because traditional pattern-matching techniques can no longer be applied to its
messages. However, TLS also introduces a complex set of observable data
features that allow many inferences to be made about both the client and the
server. We show that these features can be used to detect and understand
malware communication, while at the same time preserving the privacy of benign
uses of encryption. These data features also allow for accurate malware family
attribution of network communication, even when restricted to a single,
encrypted flow.
To demonstrate this, we performed a detailed study of how TLS is used by
malware and enterprise applications. We provide a general analysis on millions
of TLS encrypted flows, and a targeted study on 18 malware families composed of
thousands of unique malware samples and ten-of-thousands of malicious TLS
flows. Importantly, we identify and accommodate the bias introduced by the use
of a malware sandbox. The performance of a malware classifier is correlated
with a malware family's use of TLS, i.e., malware families that actively evolve
their use of cryptography are more difficult to classify.
We conclude that malware's usage of TLS is distinct from benign usage in an
enterprise setting, and that these differences can be effectively used in rules
and machine learning classifiers.

本研究表明，TLS 的数据特征可以用于检测和理解恶意软件通信，同时保护加密的良好使用隐私。此外，这些特征还可以实现准确的恶意软件家族网络通信归属，即使限制在单个加密流中。