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LingBot-Depth 2.0 fills glass and mirror RGB-D failures using self-supervised vision backbones (Apache-2.0)
Found this demo on their project page showing exactly the transparent-surface problem that breaks most RGB-D setups. Raw sensor depth drops to nothing on the glass panel, and the completion model fills it in from the backbone features. Only the four vision encoders went up on HuggingFace and GitHub this week under Apache-2.0; the depth completion weights themselves are not released. Their paper lists NYUv2 RMSE of 0.296 for the flagship ViT-g, and they report 2.552 on KITTI, trailing both DINOv3-7B and V-JEPA 2.1. For actual robotics work this is the exact failure mode that makes wine glasses and steel cabinets a consistent headache for grasp pipelines. Curious how people see validating these depth numbers when the completion weights are not available for independent testing. submitted by /u/Savings-Display5123 [link] [Kommentare] reddit.com · reddit.com
Found this demo on their project page showing exactly the transparent-surface problem that breaks most RGB-D setups. Raw sensor depth drops to nothing on the glass panel, and the completion model fills it in from the backbone features. Only the four vision encoders went up on HuggingFace and GitHub this week under Apache-2.0; the depth completion weights themselves are not released. Their paper lists NYUv2 RMSE of 0.296 for the flagship ViT-g, and they report 2.552 on KITTI, trailing both DINOv3-7B and V-JEPA 2.1. For actual robotics work this is the exact failure mode that makes wine glasses and steel cabinets a consistent headache for grasp pipelines. Curious how people see validating these depth numbers when the completion weights are not available for independent testing. submitted by /u/Savings-Display5123 [link] [Kommentare]
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