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

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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.

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

WeChat: CVer 2026-08-24 doi:10.1007/s11263-026-02997-8
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:28:21.024920 UTC

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.
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