How Tesla FSD Sees the World Without LIDAR: Vision Depth Estimation Explained | Taha Abbasi

Taha Abbasi provides technical analysis of how Tesla’s vision depth estimation works — perceiving 3D space using only cameras, without LIDAR or radar.

The Challenge

When Tesla removed radar in 2021 and committed to vision-only, the industry was skeptical. LIDAR gives 3D directly. Cameras give flat images. As Taha Abbasi has tracked through FSD versions, the answer lies in increasingly sophisticated neural architectures.

How It Works

Stereo Vision: Eight cameras triangulate distance by comparing object appearance from different angles — human binocular vision extended to surround view.

Monocular Depth: Neural networks estimate depth from learned cues — objects higher in frame are farther, known objects have predictable sizes at distance.

Temporal Integration: As the vehicle moves, position shifts reveal distance. Analyzing scene changes over time builds increasingly accurate 3D models.

Occupancy Networks: Voxel-based 3D representation classifies every point as occupied or free, navigating obstacles of any shape — not just predefined categories.

Practical Implications

Lower cost (cameras cost dollars vs thousands for LIDAR), scalability (every Tesla already equipped), continuous improvement from fleet data, and real-world robustness in rain, fog, and snow where LIDAR struggles.

The Convergence

The practical difference between vision-only and LIDAR systems narrows as neural networks improve. Taha Abbasi sees the question shifting: not whether cameras can match LIDAR, but whether the AI processing camera data can extract equivalent or superior spatial information. The evidence increasingly says yes.

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About the Author: Taha Abbasi is a technology executive, CTO, and applied frontier tech builder. Read more on Grokpedia | YouTube: The Brown Cowboy | tahaabbasi.com