DepthART: Scaling Foundation Monocular Depth to Tiny Models
Recent geometric foundation models (e.g., Metric3D, Depth Anything and UniDepth) have substantially improved monocular depth estimation (MDE) in both cross-scene generalization and metric-scale prediction, yet these gains have not translated to tiny models. We bridge this gap with DepthART (Depth Anything Rethought for Tiny Models), which is a compact MDE model for on-device deployment across diverse scenes. We first identify two capacity-driven bottlenecks in tiny models: (i) overfitting to dataset-specific distribution bias and (ii) unstable metric adaptation under camera shift, where full f
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- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%thedotmack/claude-mem →
“Shared author/contributor keys: ming”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Feng Xue →
“DepthART: Scaling Foundation Monocular Depth to Tiny Models”
- LinkedLinked via arxiv author · 85%Wu Chen →
“DepthART: Scaling Foundation Monocular Depth to Tiny Models”
- LinkedLinked via arxiv author · 85%Mingshuai Zhao →
“DepthART: Scaling Foundation Monocular Depth to Tiny Models”