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.