Signal Processing for Point Cloud based on Deep Learning

Point Cloud Upsampling for Accurate Surface Reconstruction via Attention-Guided Generation 

Reconstructing accurate surfaces from sparse point clouds remains a fundamental challenge in 3D vision. In this paper, we propose a novel upsampling framework, called Surf-PU, that focuses on surface-aware point generation. Our method comprises two key components. First, we introduce an adaptive query generation module that analyzes local attention information to determine where new points should be placed. These query points guide the generation toward regions of geometric significance. Second, we incorporate the Hyper Chamfer Distance (HCD) loss, which accounts for the distribution of point-wise distances to better capture complex surface structures. Unlike existing methods, our approach effectively combines attention-based guidance and loss-driven precision, leading to more accurate surface reconstruction. Extensive experiments on PU-GAN and PU1K datasets demonstrate that our method consistently outperforms state-of-the-art techniques, especially in terms of the P2F metric. Moreover, the proposed framework maintains robust performance across arbitrary upsampling ratios and under random noise, confirming its reliability and generalizability. 

Image Editing based on Diffusion Algorithm

Stepwise Attention Control for Training-Free Image Editing

Text-conditioned image editing aims to modify visual content based on natural language prompts while preserving the spatial structure of the input image. This capability plays a vital role in applications such as creative media design, virtual scene generation, and interactive image manipulation. However, achieving both semantic fidelity and structural preservation remains a major challenge in diffusionbased approaches. Many existing methods exhibit an imbalance between preserving the input image’s structure and reflecting the semantic intent of the prompt, often resulting in suboptimal editing outcomes. To address this problem, we propose a training-free method called Stepwise Attention Control, which divides the denoising process into four stages based on the diffusion time step and applies tailored attention control strategies to each stage. This stage-wise approach enables balanced control of both the structural fidelity and semantic consistency during the editing process. Experimental results demonstrate that the proposed method achieves more faithful semantic edits while more effectively retaining the structural features of the original image compared to existing techniques.