A 3D point cloud completion task is to generate completed 3D objects given partial observations. Auto-encoder-based models suffer from poor generalization ability to untrained 3D data. Current diffusion-based models add isotropic noise with the same variance in three x, y, z axes. More importantly, these models ignore real-world anisotropic evolution properties of 3D particles from a non-equilibrium state to thermodynamic equilibrium in the real physical world due to the velocity and energy thermodynamics of the particles, leading to unstable completions of 3D object topology. This paper presents a novel physically-based anisotropic 3D diffusion model (3DDM) to address these issues. We also present derivations of our proposed forward and reverse processes and a loss function in closed form, thus reproducibility. The 3DDM contains anisotropic energy-aware forward and reverse processes with a novel anisotropic quadratic loss function. The forward process adds anisotropic 3D Gaussian noises per-axis and mimics the thermal non-equilibrium evolution towards Maxwellian equilibrium based on velocity and kinetic energy evolutions of 3D particles in the real physical space. The reverse process learns to denoise along per-axis and per-timestep anisotropically. The anisotropic quadratic loss function penalizes errors along certain axes, yielding a highly flexible and anisotropic reverse diffusion process and a physically realistic generative model. The 3DDM denoises along x, y, z axes with different velocities from the non-equilibrium evolution, achieving fewer than 20 diffusion steps and strong generalization to unseen 3D objects and real-world scenes that were not trained.

Saliency analysis is an important implement for remote sensing image processing. It can effectively solve the contradiction between accuracy and computation complexity when it is applied in the region of interest (ROI) detection and extraction for remote sensing images. In this paper, we propose an efficient ROI detection model for remote sensing images based on low-level contrast feature saliency analysis. For the proposed model, we first perform fast directional integer wavelet transform (FD-IWT) to obtain multi-scale approximate and detail coefficients. Then these multi-scale orientation, local contrast, and global contrast features are exploited to generate the saliency map. Qualitative and quantitative evaluation shows that the proposed model outperforms the other nine state-of-art ROI detection models for that the proposed model can obtain highlighted and integrated ROI with well-defined boundaries, as well as eliminate the shadow interference in remote sensing image.