Physically Grounded Monocular Depth via Nanophotonic Wavefront Encoding

arXiv:2503.15770 · physics.optics, cs.AR, cs.CV · Submitted 2025-03-20 · Read on arXiv

physics.optics, cs.AR, cs.CV

Submitted: 2025-03-20

Updated: 2026-09-17

Terminology

• Foundation Model — 18× in this paper · explained in 3 episodes
• alpha — 18× in this paper · explained in 1 episode
• Alignment — 16× in this paper · explained in 1 episode
• Aperture — 16× in this paper · explained in 1 episode
• Ablation — 14× in this paper · explained in 1 episode
• Adaptation — 14× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Foundation Models — 9× in this paper · explained in 7 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Point Spread Function — 8× in this paper
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Benchmark — 6× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Sim-to-Real Gap — 5× in this paper · explained in 6 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• Data Augmentation — 4× in this paper · explained in 7 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• ACM Transactions on Graphics — 3× in this paper
• Fisher Information — 3× in this paper · explained in 3 episodes
• Inverse Problem — 3× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Fast Fourier Transform — 2× in this paper
• IEEE International Conference on Computational Photography — 2× in this paper
• Optical Flow — 2× in this paper · explained in 2 episodes
• Sim-to-Real Transfer — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Dense Prediction Transformer — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
• Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
• Depth Anything 3: Recovering the Visual Space from Any Views
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
• UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
• Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues
• MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

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