HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning
通过融合五个公开数据源，利用ShotNet评估投篮价值，并采用深度限制的期望最大化搜索解决进攻决策树，实现针对对手的实时篮球比赛规划。
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DINO-RotateMatch: A Rotation-Aware Deep Framework for Robust Image Matching in Large-Scale 3D Reconstruction
To address the poor robustness of feature matching under arbitrary rotations in large-scale Internet-image 3D reconstruction, this paper proposes a rotation-aware deep learning matching framework. Methodologically, it integrates self-supervised DINO-based semantic retrieval with rotation-augmented local feature matching: a data-adaptive image-pairing strategy is introduced, coupled with rotation-invariant keypoint detection (ALIKED) and orientation-sensitive feature description, and efficient matching is achieved via LightGlue. Key innovations include rotation-aware keypoint extraction, orientation-enhanced local descriptor modeling, and synergistic optimization combining semantic guidance with geometric constraints. Evaluated on the Kaggle Image Matching Challenge 2025, the method achieves second place (47th out of 943 teams), with significant improvement in mean Average Accuracy (mAA), demonstrating high accuracy, strong robustness under complex viewpoint variations, and excellent scalability.
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HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning
通过融合五个公开数据源，利用ShotNet评估投篮价值，并采用深度限制的期望最大化搜索解决进攻决策树，实现针对对手的实时篮球比赛规划。
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DINO-RotateMatch: A Rotation-Aware Deep Framework for Robust Image Matching in Large-Scale 3D Reconstruction
To address the poor robustness of feature matching under arbitrary rotations in large-scale Internet-image 3D reconstruction, this paper proposes a rotation-aware deep learning matching framework. Methodologically, it integrates self-supervised DINO-based semantic retrieval with rotation-augmented local feature matching: a data-adaptive image-pairing strategy is introduced, coupled with rotation-invariant keypoint detection (ALIKED) and orientation-sensitive feature description, and efficient matching is achieved via LightGlue. Key innovations include rotation-aware keypoint extraction, orientation-enhanced local descriptor modeling, and synergistic optimization combining semantic guidance with geometric constraints. Evaluated on the Kaggle Image Matching Challenge 2025, the method achieves second place (47th out of 943 teams), with significant improvement in mean Average Accuracy (mAA), demonstrating high accuracy, strong robustness under complex viewpoint variations, and excellent scalability.
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