The proliferation of Light Detection and Ranging (LiDAR) technology has facilitated the acquisition of three-dimensional point clouds, which are integral to applications in VR, AR, and Digital Twin. Oriented normals, critical for 3D reconstruction and scene analysis, cannot be directly extracted from scenes using LiDAR due to its operational principles. Previous traditional or learning-based methods are prone to inaccuracies due to uneven distribution and noise due to the dependence on local geometry features. This paper addresses the challenge of estimating oriented point normals by introducing a point cloud normal estimation framework via hybrid angular and Euclidean distance encoding (HAE). Our method overcomes the limitations of local geometric information by combining angular and Euclidean spaces to extract features from both point cloud coordinates and light rays, leading to more accurate normal estimation. The core of our network consists of an angular distance encoding module, which leverages both ray directions and point coordinates for unoriented normal refinement, and a ray feature fusion module for normal orientation, that is robust to noise. We also provide a point cloud dataset with ground truth normals, generated a virtual scanner, which reflects real scanning distributions and noise profiles.

Parametric edge reconstruction for point cloud data is a fundamental problem in computer graphics. Existing methods first classify points as either edge points (including corners) or non-edge points, and then fit parametric edges to the edge points. However, few points are exactly sampled on edges in practical scenarios, leading to significant fitting errors in the reconstructed edges. Prominent deep learning-based methods also primarily emphasize edge points, overlooking the potential of non-edge areas. Given that sparse and non-uniform edge points cannot provide adequate information, we address this challenge by leveraging neighboring segmented patches to supply additional cues. We introduce a novel two-stage framework that reconstructs edges precisely and completely via surface patch segmentation. First, we propose PCER-Net, a Point Cloud Edge Reconstruction Network that segments surface patches, detects edge points, and predicts normals simultaneously. Second, a joint optimization module is designed to reconstruct a complete and precise 3D wireframe by fully utilizing the predicted results of the network. Concretely, the segmented patches enable accurate fitting of parametric edges, even when sparse points are not precisely distributed along the model's edges. Corners can also be naturally detected from the segmented patches. Benefiting from fitted edges and detected corners, a complete and precise 3D wireframe model with topology connections can be reconstructed by geometric optimization. Finally, we present a versatile patch-edge dataset, including CAD and everyday models (furniture), to generalize our method. Extensive experiments and comparisons against previous methods demonstrate our effectiveness and superiority. We will release the code and dataset to facilitate future research.