OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds

TL;DR

OCCDiff introduces a novel latent diffusion approach in the occupancy function space for high-fidelity 3D building reconstruction from noisy LiDAR point clouds, enabling flexible, continuous, and robust surface modeling.

Contribution

The paper presents OCCDiff, a new method combining latent diffusion, a function autoencoder, and multi-modal features for improved 3D building reconstruction from noisy data.

Findings

Generates physically consistent and high-fidelity 3D building surfaces.

Robust to noise and varying point densities in LiDAR data.

Outperforms existing methods in reconstruction quality.

Abstract

A major challenge in reconstructing buildings from LiDAR point clouds lies in accurately capturing building surfaces under varying point densities and noise interference. To flexibly gather high-quality 3D profiles of the building in diverse resolution, we propose OCCDiff applying latent diffusion in the occupancy function space. Our OCCDiff combines a latent diffusion process with a function autoencoder architecture to generate continuous occupancy functions evaluable at arbitrary locations. Moreover, a point encoder is proposed to provide condition features to diffusion learning, constraint the final occupancy prediction for occupancy decoder, and insert multi-modal features for latent generation to latent encoder. To further enhance the model performance, a multi-task training strategy is employed, ensuring that the point encoder learns diverse and robust feature representations. Empirical results show that our method generates physically consistent samples with high fidelity to the target distribution and exhibits robustness to noisy data.