The primal sketch is a fundamental representation in Marr's vision theory,
which allows for parsimonious image-level processing from 2D to 2.5D
perception. This paper takes a further step by computing 3D primal sketch of
wireframes from a set of images with known camera poses, in which we take the
2D wireframes in multi-view images as the basis to compute 3D wireframes in a
volumetric rendering formulation. In our method, we first propose a NEural
Attraction (NEAT) Fields that parameterizes the 3D line segments with
coordinate Multi-Layer Perceptrons (MLPs), enabling us to learn the 3D line
segments from 2D observation without incurring any explicit feature
correspondences across views. We then present a novel Global Junction
Perceiving (GJP) module to perceive meaningful 3D junctions from the NEAT
Fields of 3D line segments by optimizing a randomly initialized
high-dimensional latent array and a lightweight decoding MLP. Benefitting from
our explicit modeling of 3D junctions, we finally compute the primal sketch of
3D wireframes by attracting the queried 3D line segments to the 3D junctions,
significantly simplifying the computation paradigm of 3D wireframe parsing. In
experiments, we evaluate our approach on the DTU and BlendedMVS datasets with
promising performance obtained. As far as we know, our method is the first
approach to achieve high-fidelity 3D wireframe parsing without requiring
explicit matching.

通过计算已知相机姿态下的图像组的三维原始草图，并利用神经吸引字段和全局连接感知模块，该方法提出了一种高保真度的解析三维线框的方法。

基于神经吸引场的体素线框分析

Volumetric Wireframe Parsing from Neural Attraction Fields

In this paper we present the Zeitview Rooftop Geometry (ZRG) dataset. ZRG
contains thousands of samples of high resolution orthomosaics of aerial imagery
of residential rooftops with corresponding digital surface models (DSM), 3D
rooftop wireframes, and multiview imagery generated point clouds for the
purpose of residential rooftop geometry and scene understanding. We perform
thorough benchmarks to illustrate the numerous applications unlocked by this
dataset and provide baselines for the tasks of roof outline extraction,
monocular height estimation, and planar roof structure extraction.

本文介绍了 Zeitview Rooftop Geometry（ZRG）数据集，包含成千上万个高分辨率空照图像的采样，用于住宅屋顶几何和场景理解的数字地面模型（DSM），3D 屋顶线框和多视角图像生成点云，并进行了充分的基准测试，以说明此数据集解锁的众多应用程序，并为屋顶轮廓提取、单眼高度估计和平面屋顶结构提取任务提供基线。