@rohanpaul_ai: The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error j…
Summary
LingBot-Depth 2.0 is a depth-completion model that halves depth error, excels on transparent objects like glass and mirrors, and tops 12 out of 16 benchmarks, powered by the open-source LingBot-Vision backbone.
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Cached at: 07/08/26, 12:24 AM
The robot’s “eyes” just received a big upgrade.
LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped.
Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline.
A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation.
LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud.
Numbers on LingBot-Depth 2.0
- Excels on glass, mirrors & transparent objects — where traditional depth cameras fail
- Training data: 3M → 150M (50x scale-up)
- 12 out of 16 first-place rankings on depth completion benchmarks
- RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes
LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale.
The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.
Robbyant (@robbyant_brain): 🪞 Glass. Mirrors. Transparent objects. — The nightmare of every depth camera. We just solved it! Introducing LingBot-Depth 2.0: 150M-scale training, half the depth error, 12/16 benchmarks topped. Powered by LingBot-Vision — the visual foundation model behind Depth’s
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