PBWR: Parametric Building Wireframe Reconstruction from Aerial LiDAR Point Clouds (2311.12062v1)

Abstract: In this paper, we present an end-to-end 3D building wireframe reconstruction method to regress edges directly from aerial LiDAR point clouds.Our method, named Parametric Building Wireframe Reconstruction (PBWR), takes aerial LiDAR point clouds and initial edge entities as input, and fully uses self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarityto remove redundant edges. Additionally, a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters, where simple use of edge distance loss isn't suitable. In our experiments, we demonstrate state-of-the-art results on the Building3D dataset, achieving an improvement of approximately 36% in entry-level dataset edge accuracy and around 42% improvement in the Tallinn dataset.

• The paper presents a novel method that bypasses intermediate corner prediction by directly regressing edge parameters using transformers.
• It employs tailored edge similarity metrics and an edge non-maximum suppression technique to enhance wireframe accuracy.
• Comprehensive experiments show up to 42% improvement in edge accuracy, highlighting its potential for urban modeling and digital twin applications.

Parametric Building Wireframe Reconstruction (PBWR) from Aerial LiDAR Point Clouds

Introduction

The exploration of 3D wireframe models from aerial LiDAR point clouds holds substantial promise for advancements in metaverse, smart cities, and virtual reality applications. Given the lightweight nature and comprehensive representational capacity of 3D wireframe models, their accurate and efficient reconstruction is paramount. Despite the inherent fidelity of point clouds to depict structural details, the current methodologies relying on intermediate processing steps, such as corner prediction and edge classification, are limited. These methods often result in the accumulation of errors and subsequently impede further advancements in the field.

Related Work

The literature review underscores two predominant realms of building reconstruction: optimization-based algorithms focusing on polygonal meshes and methods reliant on the Manhattan World assumption to enforce orthogonal plane directions for dense mesh generation. Notably, deep learning approaches have ventured into wireframe reconstruction, primarily treating it as an edge point classification task, thereby predicting object contours. However, the complexity of accurately obtaining corner or edge coordinates from intricate scenes remains a daunting challenge, evidencing a need for refined approaches.

Proposed Method

The PBWR method introduces a transformative approach by directly regressing edge parameters from aerial LiDAR point clouds without succumbing to intermediate processes like corner prediction or edge classification. Utilizing the self-attention mechanism of transformers, PBWR adaptively regresses edge parameters, significantly mitigating the generation of extraneous edges and enhancing the fidelity of the wireframe model.

Key Contributions

1. A novel wireframe reconstruction strategy that circumvents heuristic-guided intermediate processes, streamlining the creation of 3D wireframe models.
2. The incorporation of edge similarity and a tailored edge loss function, fostering the accurate regression of edges in point clouds.
3. The development of an edge non-maximum suppression (E-NMS) method based on edge similarity, a pioneering application in wireframe reconstruction tasks.

Technical Highlights

• The utilization of transformers to directly regress edge parameters from point clouds, significantly reducing the generation of redundant edges.
• The introduction of an edge similarity method encompassing Hausdorff distance, edge length, and cosine similarity to augment bipartite matching during edge regression.
• Comprehensive experiments yielding significant improvements in wireframe reconstruction accuracy, notably a 36% enhancement in edge accuracy on the entry-level dataset and a 42% improvement on the Tallinn dataset.

Implications and Future Directions

The research introduces a paradigm shift in building wireframe reconstruction by eliminating the need for intermediate heuristic-guided processes, thereby reducing error accumulation and enhancing model representativeness. This approach not only enhances the efficiency and accuracy of wireframe reconstruction but also opens avenues for further exploration into direct parameter regression methods in 3D point cloud processing.

The substantial improvements in model accuracy and the reduction of computational complexity present PBWR as a robust methodology for large-scale urban modeling projects. Future explorations could delve into refining the edge similarity metrics and exploring the integration of PBWR with other point cloud processing tasks to foster advancements in digital twin technologies, urban planning, and heritage conservation.

In conclusion, the novel PBWR model marks a significant advancement in wireframe reconstruction from aerial LiDAR point clouds, offering a sophisticated yet efficient methodology that holds profound implications for the burgeoning fields of virtual reality, smart city planning, and 3D visualization.