Shang Wang

This work proposes RIM (Retrieval-In-Matching), which renders UAV-viewpoint references from Google 3D Tiles across locations, altitudes, and orientations, adapts SALAD in two stages with pose-near positives and geographically distant hard negatives and then re-ranks Top-K candidates by local geometric consistency, and further freezes the adapted DINOv2-B retriever while distilling a local-descriptor decoder from its token field and a shallow VGG19 detail stream so that one query-side DINOv2-B forward supports both SALAD retrieval and local description; evaluated zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data, RIM outperforms ten retrieval baselines, improving Recall@1 over SALAD by 8.55/13.

This work proposes RIM (Retrieval-In-Matching), which renders UAV-viewpoint references from Google 3D Tiles across locations, altitudes, and orientations, adapts SALAD in two stages with pose-near positives and geographically distant hard negatives and then re-ranks Top-K candidates by local geometric consistency, and further freezes the adapted DINOv2-B retriever while distilling a local-descriptor decoder from its token field and a shallow VGG19 detail stream so that one query-side DINOv2-B forward supports both SALAD retrieval and local description; evaluated zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data, RIM outperforms ten retrieval baselines, improving Recall@1 over SALAD by 8.55/13.