@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.
View Cached Full Text
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
Similar Articles
@heyshrutimishra: Nobody talks about this but every robot on the market is blind to glass. Put a mirror in front of it. A glass bottle. I…
LingBot-Depth 2.0, trained on 150M samples, solves the longstanding problem of robots being blind to glass and transparent objects, achieving top performance on 12/16 depth benchmarks and halving depth error. Ant Group used it to significantly improve their robots' perception.
@rohanpaul_ai: A 1B-parameter vision model just beat a 7B one on depth, frozen, single linear layer, zero fine-tuning. @robbyant_brain…
Robbyant releases LingBot-Vision, a 1B-parameter vision model trained on boundaries that achieves better depth estimation than DINOv3-7B, with open weights.
@robbyant_brain: LingBot-VLA 2.0 is now open-source — our next-gen embodied foundation model. 60,000 hours of high-quality pretraining d…
LingBot-VLA 2.0, an open-source embodied foundation model, has been released with 60,000 hours of pretraining data supporting 20 robot configurations across 17 brands, capable of sub-130ms inference on RTX 4090.
@AdinaYakup: LingBot Vision A self-supervised vision backbone family for dense spatial perception from Ant Group @robbyant_brain - A…
LingBot Vision, a self-supervised vision backbone family from Ant Group, uses masked boundary modeling to achieve state-of-the-art performance on dense spatial perception tasks, beating the larger DINOv3 model on NYU-Depth v2.
@rohanpaul_ai: Most video-action robot models are a content-creation video generator with an action module attached. LingBot-VA 2.0 fr…
LingBot-VA 2.0 is a video-action foundation model trained from scratch for robot control, achieving 225 Hz closed-loop execution with 13B parameters (1.9B active per token) and outperforming prior models on RoboTwin 2.0.