3D Gaussian Splatting (3DGS) has achieved excellent rendering quality with fast training and rendering speed. However, its optimization process lacks explicit geometric constraints, leading to suboptimal geometric reconstruction in regions with sparse or no observational input views. In this work, we try to mitigate the issue by incorporating a pre-trained matching prior to the 3DGS optimization process. We introduce Flow Distillation Sampling (FDS), a technique that leverages pre-trained geometric knowledge to bolster the accuracy of the Gaussian radiance field. Our method employs a strategic sampling technique to target unobserved views adjacent to the input views, utilizing the optical flow calculated from the matching model (Prior Flow) to guide the flow analytically calculated from the 3DGS geometry (Radiance Flow). Comprehensive experiments in depth rendering, mesh reconstruction, and novel view synthesis showcase the significant advantages of FDS over state-of-the-art methods. Additionally, our interpretive experiments and analysis aim to shed light on the effects of FDS on geometric accuracy and rendering quality, potentially providing readers with insights into its performance.

Optical remote sensing image salient object detection (ORSI-SOD) poses significant challenges due to complicated object variances and interfering surroundings. Although existing methods have achieved impressive performance, they encounter difficulties in balancing deep and shallow features, leading to limitations in preserving object integrity and edge detail. To address this, we propose the Integrated and Detailed Ensemble Learning (IDEL) framework, which incorporates hierarchical branches with deep supervision. By divide-and-conquer, each branch captures information with a specific granularity, while the fusion module combines all outputs to generate the final saliency maps. To ensure the effectiveness of ensemble learning, IDEL is designed to satisfy two necessary conditions: the weak learner property and branch independence. Firstly, we utilize the Transformer blocks with a global receptive field and purify intermediate features with the Deep Supervision Module (DSM) to enhance the performance of each branch. Secondly, we disentangle multiple branches through hardness-aware weights and hierarchical supervision labels, allowing them to learn distinct features. Qualitative visualizations demonstrate the effectiveness of each module, and extensive experimental results conducted on three popular ORSI datasets confirm the superiority of IDEL compared to other state-of-the-art (SOTA) counterparts.