顶刊IJCV 2026！TEPR：热边缘提示引导的多视图 3D 重建几何学习

TL;DR - The IJCV paper introduces TEPR, an edge-prompted geometry-learning framework for multi-view 3D reconstruction from thermal imagery. It improves reconstruction in darkness, smoke, and low-light environments where RGB methods struggle and thermal images lack reliable texture.

• TEPR combines thermal appearance with learned edge prompts, modality-adaptive layers, and top-k sparse attention to improve cross-view matching and suppress noisy associations.
• Edge-enhanced prediction uses global multimodal features for camera parameters and dense edge features for depth boundaries, while hierarchical depth alignment enforces local-to-multiview geometric consistency.
• The authors built TI-FRD with 38,840 thermal frames across 30 scenes, including daytime, nighttime, and smoke sequences with multimodal sensor data and ground truth.
• Against VGGT on TI-FRD, TEPR reduced average reconstruction accuracy and completeness errors by about 38% and 33%, respectively, and trajectory error by about 44%, while running at 19.5 FPS.