Yacine's Real-Time Stereo Depth: 60fps Streaming with 10ms Inference

yacineMTB (Yacine Atie) demonstrated a real-time stereo depth estimation system based on Fast Foundation Stereo: depth maps are generated at 60fps and streamed wirelessly as pixel data over WiFi UDP. Inference takes about 10ms with an end-to-end latency target of 20ms; after aggressive optimization, p99 latency has dropped to roughly 80ms and is nearly imperceptible.

Confirmed

• Depth estimation runs at 60fps, streamed over WiFi UDP.
• Inference takes 10ms; end-to-end target is 20ms; reposts cite 80ms p99 latency.
• The author clarified that stuttering and tearing in the demo video came from screen-recording software interfering with the renderer, not the depth generation itself (Twitter's encoding also hurt quality).
• He is rewriting the Allwinner WiFi driver to stop sending ACK packets for true UDP transport, paired with a custom ultra-low-latency video codec, aiming to have a drone perform a real backflip by Labor Day.
• Related engineering: a custom global-shutter camera driver that scans fewer rows for higher FPS, and a custom rendering engine that trains policies on depth pixels—in as little as 2 minutes when熟练.
• Plans to add event-triggered mosquito detection.

Why it matters

• His key lesson: most latency comes from low-level engineering issues (his "dumb mistakes"), not inference. For real-time robot vision developers, pipeline engineering often pays off more than faster models.
• The full-stack DIY approach—WiFi driver, camera driver, rendering/training engine—points toward practical ultra-low-latency perception for drones and robots.

2026-08-27 ~ 2026-08-27 · 9 related posts

• [source] 60fps depth estimation achieved targeting 20ms end-to-end latency — yacineMTB · 2026-08-27
• 60fps depth streamed over WiFi UDP at 10ms inference, targeting 20ms end-to-end — astridwilde1 · 2026-08-27
• [source] Hacking WiFi driver for ultra-low latency drone control — yacineMTB · 2026-08-27
• Writing custom drivers to boost global shutter camera FPS — yacineMTB · 2026-08-27
• Train policy from depth pixels in 2 minutes via custom engine — yacineMTB · 2026-08-27
• Latency reduced to imperceptible levels via aggressive non-inference optimization — yacineMTB · 2026-08-27
• yacine gets real-time stereo depth latency down to imperceptible levels — yacineMTB · 2026-08-27
• Yacine demos depth generation video; stuttering is his recorder, not the model — yacineMTB · 2026-08-27
• [source] Robot Vision Latency Squeezed to 80ms p99 — Most Came From Dumb Things, Not Inference — REVOLVO_OCELOTS · 2026-08-27