Reverse engineering CAD models from raw geometry is a classic but challenging
research problem. In particular, reconstructing the CAD modeling sequence from
point clouds provides great interpretability and convenience for editing. To
improve upon this problem, we introduce geometric guidance into the
reconstruction network. Our proposed model, PS-CAD, reconstructs the CAD
modeling sequence one step at a time. At each step, we provide two forms of
geometric guidance. First, we provide the geometry of surfaces where the
current reconstruction differs from the complete model as a point cloud. This
helps the framework to focus on regions that still need work. Second, we use
geometric analysis to extract a set of planar prompts, that correspond to
candidate surfaces where a CAD extrusion step could be started. Our framework
has three major components. Geometric guidance computation extracts the two
types of geometric guidance. Single-step reconstruction computes a single
candidate CAD modeling step for each provided prompt. Single-step selection
selects among the candidate CAD modeling steps. The process continues until the
reconstruction is completed. Our quantitative results show a significant
improvement across all metrics. For example, on the dataset DeepCAD, PS-CAD
improves upon the best published SOTA method by reducing the geometry errors
(CD and HD) by 10%, and the structural error (ECD metric) by about 15%.

通过将几何指导引入重建网络中，我们提出了一种名为 PS-CAD 的模型，它逐步重建 CAD 建模序列，并通过提供几何表面的几何信息和平面提示来改进重建的效果。在 DeepCAD 数据集上，PS-CAD 在所有度量指标上都有明显的改进，减少了几何错误（CD 和 HD）约 10%，结构错误（ECD 度量）约 15%。

PS-CAD: 基于提示和选择的 CAD 重建中的局部几何引导

PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD  Reconstruction

Despite the remarkable progress facilitated by learning-based stereo-matching
algorithms, disparity estimation in low-texture, occluded, and bordered regions
still remains a bottleneck that limits the performance. To tackle these
challenges, geometric guidance like plane information is necessary as it
provides intuitive guidance about disparity consistency and affinity
similarity. In this paper, we propose a normal incorporated joint learning
framework consisting of two specific modules named non-local disparity
propagation(NDP) and affinity-aware residual learning(ARL). The estimated
normal map is first utilized for calculating a non-local affinity matrix and a
non-local offset to perform spatial propagation at the disparity level. To
enhance geometric consistency, especially in low-texture regions, the estimated
normal map is then leveraged to calculate a local affinity matrix, providing
the residual learning with information about where the correction should refer
and thus improving the residual learning efficiency. Extensive experiments on
several public datasets including Scene Flow, KITTI 2015, and Middlebury 2014
validate the effectiveness of our proposed method. By the time we finished this
work, our approach ranked 1st for stereo matching across foreground pixels on
the KITTI 2015 dataset and 3rd on the Scene Flow dataset among all the
published works.

通过几个公开数据集上的广泛实验，我们提出的方法在 KITTI 2015 数据集的前景像素立体匹配中排名第一，在 Scene Flow 数据集中排名第三，验证了我们方法的有效性。