Restoring Roof Height Maps from Severely Corrupted Point Data using Diffusion-based Techniques

Conceptos Básicos

RoofDiffusion is a novel diffusion-based technique that can robustly complete and denoise roof height maps, even under extreme conditions of sparsity, regional incompleteness, and noise.

Resumen

The paper introduces RoofDiffusion, a diffusion model-based approach for restoring roof height maps from severely corrupted point data. The key highlights are:

1. RoofDiffusion can handle up to 99% missing data points and 80% regional incompleteness, while remaining resilient to tree occlusion noise. It outperforms state-of-the-art depth completion and DEM inpainting methods on both a roof-specific benchmark and the BuildingNet dataset.

2. The authors propose a novel "tree planting" method for simulating tree occlusion noise, and a multi-Gaussian masking technique for synthesizing incompleteness in roof height maps. These enable data augmentation for self-supervised learning and benchmark creation.

3. The paper introduces the PoznanRD dataset, featuring 13k high-detail Level of Detail (LoD) 2.2 noise-free roof meshes and height maps, to address the "long tail" of complex roof geometries.

4. Experiments show that using RoofDiffusion as a preprocessing step significantly improves the accuracy of 3D building reconstruction algorithms like City3D.

5. Qualitative results demonstrate the effectiveness of RoofDiffusion on real-world datasets like AHN3, Dales3D, and USGS 3DEP LiDAR, handling various types of corruption.

Estadísticas

Factors like low sensor resolution and poor surface reflectance can cause low point density in roof height maps.
Portions of roof data can be missing due to environmental interference, occlusions by taller surrounding objects, or non-orthogonal camera angles.
Intrusions on building footprints, such as trees, can lead to incorrect reconstruction, resulting in artifacts like non-existing dormers.

Citas

"RoofDiffusion is a new end-to-end self-supervised diffusion technique for robustly completing, in particular difficult, roof height maps."
"RoofDiffusion leverages widely-available curated footprints and can so handle up to 99% point sparsity and 80% roof area occlusion (regional incompleteness)."

Ideas clave extraídas de

RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion

by Kyle... a las arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09290.pdf

Consultas más profundas

How can the proposed "tree planting" and multi-Gaussian masking techniques be extended to simulate other types of real-world corruptions in roof height maps, such as sensor noise or environmental factors?

The "tree planting" technique for simulating tree occlusion and the multi-Gaussian masking method for generating incomplete regions in roof height maps can be extended to simulate other real-world corruptions by adapting the underlying principles to different types of disturbances. For instance, to simulate sensor noise, a similar approach could involve introducing random perturbations or errors in the height values of the points, mimicking the inaccuracies that can arise from sensor limitations or calibration issues. Environmental factors like dust or atmospheric interference could be simulated by introducing additional layers of noise or distortion in the height map data.
By adjusting the parameters of the Gaussian masks, it is possible to replicate various types of environmental factors that may affect the accuracy of roof height maps. For example, to simulate the impact of dust or haze, the variance of the Gaussian masks could be increased to create a more diffuse and blurred effect on the height map. Similarly, to model sensor noise, the mean and variance of the Gaussian masks could be manipulated to introduce random fluctuations in the height values, reflecting the uncertainty associated with sensor measurements.
Overall, by customizing the parameters and characteristics of the tree planting and Gaussian masking techniques, researchers can effectively simulate a wide range of real-world corruptions in roof height maps, enabling more comprehensive testing and evaluation of algorithms designed to handle such challenges.

What other architectural or urban planning applications could benefit from the accurate and robust roof height map restoration enabled by RoofDiffusion?

The accurate and robust roof height map restoration facilitated by RoofDiffusion has the potential to benefit various architectural and urban planning applications beyond the scope of the research presented. Some of the key areas that could leverage this technology include:

Urban Development and Planning: Roof height maps are essential for urban planners to assess building density, plan infrastructure projects, and optimize city layouts. Accurate height maps can provide valuable insights into the urban landscape, enabling better decision-making in urban development projects.

Historical Preservation: In historical preservation efforts, detailed roof height maps can aid in the restoration and conservation of heritage buildings. By accurately reconstructing the 3D geometry of historical structures, preservationists can ensure the authenticity and integrity of architectural landmarks.

Disaster Response and Recovery: During natural disasters or emergencies, roof height maps can be instrumental in assessing damage, planning rescue operations, and coordinating recovery efforts. The ability to quickly restore and analyze height maps can enhance the efficiency of disaster response teams.

Energy Efficiency and Sustainability: By analyzing roof geometries and heights, architects and engineers can optimize building designs for energy efficiency and sustainability. Accurate height maps can help in determining solar potential, optimizing daylighting, and implementing green building practices.

Telecommunications and Infrastructure Planning: Roof height maps play a crucial role in telecommunications infrastructure planning, such as the placement of antennas and signal coverage analysis. Reliable height map restoration can support the design and deployment of communication networks.

In essence, the applications of RoofDiffusion extend beyond the realm of 3D building reconstruction to various domains where precise and reliable roof height information is essential for decision-making and planning processes.

Could the diffusion-based approach used in RoofDiffusion be adapted to handle other types of 3D data, such as point clouds or meshes, beyond just 2D height maps?

The diffusion-based approach employed in RoofDiffusion can indeed be adapted to handle other types of 3D data, such as point clouds or meshes, extending its applicability to a broader range of spatial data reconstruction tasks. By leveraging the principles of diffusion models and conditional probability, researchers can apply similar techniques to process and enhance different forms of 3D data. Here are some ways in which the diffusion-based approach could be adapted for handling point clouds or meshes:

Point Cloud Denoising: Similar to the denoising of height maps, the diffusion model can be utilized to remove noise and artifacts from point cloud data. By conditioning the diffusion process on the noisy input point cloud, the model can effectively restore clean and accurate point cloud representations.

Mesh Completion and Inpainting: For incomplete or corrupted mesh data, the diffusion-based approach can be employed to fill in missing parts and reconstruct the complete geometry. By formulating the mesh restoration task as an image inpainting problem, the model can predict the missing regions based on the available information.

Surface Reconstruction: The diffusion model can be utilized for surface reconstruction tasks, where the goal is to generate a continuous and smooth surface representation from sparse or irregularly sampled data points. By incorporating the spatial relationships between points, the model can infer the underlying surface geometry.

Feature Extraction and Segmentation: The diffusion-based approach can also be adapted for feature extraction and segmentation in point clouds or meshes. By learning the underlying structure and patterns in the data, the model can identify distinct features, segments, or regions within the 3D dataset.

Overall, the flexibility and versatility of diffusion models make them well-suited for handling various types of 3D data beyond height maps, offering a promising avenue for advancing research in spatial data processing and reconstruction.