Self-supervised depth and ego-motion learning is the joint training paradigm where depth and camera pose networks are optimized using view synthesis consistency instead of ground-truth labels - it learns 3D scene structure and motion directly from raw video sequences.

What Is Self-Supervised Depth and Ego-Motion?

• Definition: Train depth predictor and pose estimator together by minimizing reprojection error across adjacent frames.
• Supervision Source: Geometry-induced photometric consistency rather than manual depth or pose annotation.
• Typical Setup: Target frame plus source frames with differentiable warping.
• Popular Examples: Monodepth-style pipelines with pose networks.

Why It Matters

• Label-Free Scaling: Uses abundant unlabeled videos for 3D learning.
• Cost Reduction: Avoids expensive depth sensor or motion-capture ground truth.
• General Utility: Provides initialization for SLAM, navigation, and reconstruction tasks.
• Domain Adaptation: Easier transfer to new environments using in-domain video.
• Research Impact: Major driver of modern monocular depth progress.

Training Components

Depth Network:

• Predict per-pixel depth for target frame.
• Often multi-scale outputs for stable training.

Pose Network:

• Predict relative transforms between target and source frames.
• Supports differentiable view warping.

Photometric Loss Stack:

• Reprojection error, smoothness terms, and masking for dynamic objects.
• Stabilizes training in non-ideal scenes.

How It Works

Step 1:

• Predict depth and relative pose, warp source frame into target view using differentiable geometry.

Step 2:

• Minimize reconstruction error plus regularization losses and update both networks jointly.

Self-supervised depth and ego-motion learning is a scalable 3D perception strategy that turns video consistency into geometry and motion supervision - it is a powerful alternative when labeled depth and pose data are scarce.