TartanMatch: Towards Universal Dense Correspondence Across M
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: TartanMatch: Towards Universal Dense Correspondence Across Modalities. TartanMatch is a unified model for dense correspondence across all 25 ordered pairs of RGB, event, thermal, depth, and projected LiDAR modalities. It adapts an RGB-pretrained model using lightweight adaptation and real/s…
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- arXiv Robotics — research abstractsTartanMatch: Towards Universal Dense Correspondence Across Modalities
TartanMatch is a unified model for dense correspondence across all 25 ordered pairs of RGB, event, thermal, depth, and projected LiDAR modalities. It adapts an RGB-pretrained model using lightweight adaptation and real/synthetic data, achieving 61.9% and 49.6% reductions in Endpoint Error over baselines for cross-modal and same-modal matching, respectively. Joint training across modalities improves accuracy, especially for challenging cross-modal pairs.
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