Jinglei Feng | Searcharxiv

arXiv2026

Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing

Wireframe parsing aims to recover line segments and their junctions to form a structured geometric representation useful for downstream tasks such as Simultaneous Localization and Mapping (SLAM). Existing methods predict lines and junctions separately and reconcile them post-hoc, causing mismatches and reduced robustness. We present Co-PLNet, a point-line collaborative framework that exchanges spatial cues between the two tasks, where early detections are converted into spatial prompts via a Point-Line Prompt Encoder (PLP-Encoder), which encodes geometric attributes into compact and spatially aligned maps. A Cross-Guidance Line Decoder (CGL-Decoder) then refines predictions with sparse attention conditioned on complementary prompts, enforcing point-line consistency and efficiency. Experiments on Wireframe and YorkUrban show consistent improvements in accuracy and robustness, together with favorable real-time efficiency, demonstrating our effectiveness for structured geometry perception. Our code is available at https://github.com/GalacticHogrider/Co-PLNet.

cs.CV↗

arXiv2024

Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation

The rapid growth of behind-the-meter (BTM) solar power generation systems presents challenges for distribution system planning and scheduling due to invisible solar power generation. To address the data leakage problem of centralized machine-learning methods in BTM solar power generation estimation, the federated learning (FL) method has been investigated for its distributed learning capability. However, the conventional FL method has encountered various challenges, including heterogeneity, communication failures, and malicious privacy attacks. To overcome these challenges, this study proposes a communication-robust and privacy-safe distributed estimation method for heterogeneous community-level BTM solar power generation. Specifically, this study adopts multi-task FL as the main structure and learns the common and unique features of all communities. Simultaneously, it embeds an updated parameters estimation method into the multi-task FL, automatically identifies similarities between any two clients, and estimates the updated parameters for unavailable clients to mitigate the negative effects of communication failures. Finally, this study adopts a differential privacy mechanism under the dynamic privacy budget allocation strategy to combat malicious privacy attacks and improve model training efficiency. Case studies show that in the presence of heterogeneity and communication failures, the proposed method exhibits better estimation accuracy and convergence performance as compared with traditional FL and localized learning methods, while providing stronger privacy protection.

eess.SY↗