用扩散模型重建噪声点云中的高保真建筑3D结构
OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds
- 在体素占据函数空间用潜变量扩散生成连续3D形状
- 对噪声点云仍能生成物理合理、高保真的建筑表面
- 适合需要抗噪重建的三维城市建模任务
从激光雷达点云重建建筑面临点密度不均与噪声干扰的挑战。为在不同分辨率下灵活获取高质量建筑3D轮廓，我们提出OCCDiff，将潜变量扩散模型应用于占据函数空间。该方法结合潜变量扩散过程与函数自编码器架构，生成可在任意位置评估的连续占据函数。此外，设计点编码器提供条件特征，约束占据解码器预测，并注入多模态特征以指导潜变量生成。通过多任务训练策略，提升点编码器学习多样化且鲁棒的特征表示能力。实验表明，本方法生成的样本在物理上一致，与目标分布高度吻合，且对噪声数据具有强鲁棒性。
原文摘要 · Abstract (English)
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.
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