Feature extraction is a critical technology to realize the automatic
transmission of feature information throughout product life cycles. As CAD
models primarily capture the 3D geometry of products, feature extraction
heavily relies on geometric information. However, existing feature extraction
methods often yield inaccurate outcomes due to the diverse interpretations of
geometric information. This report presents a set-based feature extraction
approach to address this uncertainty issue. Unlike existing methods that seek
accurate feature results, our approach aims to transform the uncertainty of
geometric information into a set of feature subgraphs. First, we define the
convexity of basic geometric entities and introduce the concept of two-level
attributed adjacency graphs. Second, a feature extraction workflow is designed
to determine feature boundaries and identify feature subgraphs from CAD models.
This set of feature subgraphs can be used for further feature recognition. A
feature extraction system is programmed using C++ and UG/Open to demonstrate
the feasibility of our proposed approach.

基于集合的特征提取方法应用于 CAD 模型，将几何信息的不确定性转化为一组特征子图，并通过确定特征边界和识别特征子图来实现特征提取系统。

三维 CAD 模型特征提取的基于集合方法

A Set-based Approach for Feature Extraction of 3D CAD Models

It is desirable to predict the behavior of traffic participants conditioned
on different planned trajectories of the autonomous vehicle. This allows the
downstream planner to estimate the impact of its decisions. Recent approaches
for conditional behavior prediction rely on a regression decoder, meaning that
coordinates or polynomial coefficients are regressed. In this work we revisit
set-based trajectory prediction, where the probability of each trajectory in a
predefined trajectory set is determined by a classification model, and
first-time employ it to the task of conditional behavior prediction. We propose
RESET, which combines a new metric-driven algorithm for trajectory set
generation with a graph-based encoder. For unconditional prediction, RESET
achieves comparable performance to a regression-based approach. Due to the
nature of set-based approaches, it has the advantageous property of being able
to predict a flexible number of trajectories without influencing runtime or
complexity. For conditional prediction, RESET achieves reasonable results with
late fusion of the planned trajectory, which was not observed for
regression-based approaches before. This means that RESET is computationally
lightweight to combine with a planner that proposes multiple future plans of
the autonomous vehicle, as large parts of the forward pass can be reused.

本文提出了一种基于集合的轨迹预测方法，将其应用于无人驾驶车辆的条件行为预测，并通过图形编码器和计划轨迹的后期融合，实现了与回归方法相当的性能。