Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction.

Area of Science:

• Computer Vision
• 3D Geometry Processing
• Machine Learning

Background:

• Monocular depth prediction is vital for 3D scene understanding.
• Existing methods often overlook crucial 3D geometric constraints.
• Progress in evaluation metrics has not fully addressed geometric inaccuracies.

Purpose of the Study:

• To highlight the significance of high-order 3D geometric constraints in depth prediction.
• To introduce a novel loss term for improved accuracy and robustness.
• To enable learning of scale-invariant depth without metric supervision.

Main Methods:

• Incorporated a virtual normal loss term based on randomly sampled 3D points.
• Enforced geometric constraints directly within the depth estimation model.
• Trained and evaluated on diverse datasets, including a newly constructed large-scale dataset (DiverseDepth).

Main Results:

• Significantly improved accuracy and robustness in monocular depth estimation.
• Achieved state-of-the-art results on NYU Depth-V2 and KITTI datasets for metric depth.
• Demonstrated successful recovery of 3D structures (point clouds, surface normals) directly from predicted depth.
• Showcased excellent generalization capabilities with zero-shot testing on multiple datasets.

Conclusions:

• Virtual normal loss effectively integrates 3D geometric constraints into depth prediction.
• The method enhances both metric and affine-invariant depth estimation.
• Eliminates the need for additional models for 3D structure recovery.
• Offers a robust approach for depth prediction across diverse scenes and datasets.