LingBot-Depth 2.0 Tackles Transparent Object Depth Estimation
JaynitMakwana · x · 2026-07-07
The author notes that many depth benchmarks succeed in controlled environments, but the real challenge lies in how robots perform when facing surfaces like windows, mirrors, and reflective materials.
Because of this, the depth estimation results achieved by LingBot-Depth 2.0 on transparent objects caught their attention.
Related event: Ant Robbyant Open-Sources LingBot Vision Models, Topping Depth Benchmarks(18 posts)→
More from Embodied
- NVIDIA open-sources FLORA, a robotics data engine ~125x cheaper than GPT-6 Astra — nvidia · 2026-10-27
- Embodied AI startups need a different operational discipline, investor says — ___Mufasaa · 2026-10-27
- π0.5 study: raising demos from 10 to 438 lifts Place Ball in Bowl success from 7% to 83% — stingning · 2026-10-27
- GPT-6 Astra controls dexterous hands zero-shot at just 5.2% success in 500 trials — stingning · 2026-10-27
- HandsOn benchmark: 66 verified tasks and 18,000 human demos for dexterous manipulation — stingning · 2026-10-27
- DexPort: a handheld interface with a real robot hand for dexterous-hand data collection — pabbeel · 2026-10-27