depth from video

**Depth from video** is the **estimation of per-pixel scene distance by exploiting temporal parallax and multi-frame geometric consistency** - motion between frames provides strong cues about relative and absolute depth under suitable camera movement. **What Is Depth from Video?** - **Definition**: Infer depth maps using monocular or multi-view video sequences. - **Key Cue**: Parallax where closer points move more in image coordinates under camera motion. - **Model Types**: Geometry-based SfM pipelines, self-supervised monocular depth networks, and hybrid systems. - **Output Use**: 3D reconstruction, navigation, and AR scene understanding. **Why Depth from Video Matters** - **3D Awareness**: Converts 2D video into metric scene structure. - **Sensor Savings**: Enables depth estimation without dedicated depth hardware. - **Planning Support**: Essential for obstacle avoidance and spatial reasoning. - **Rendering Utility**: Depth improves compositing and view synthesis quality. - **Scalable Data**: Can train from large unlabeled video corpora via photometric constraints. **Depth Estimation Strategies** **Structure-from-Motion Geometry**: - Recover camera poses and triangulate points from feature matches. - Produces sparse or semi-dense depth. **Self-Supervised Depth Nets**: - Predict depth and pose jointly with view synthesis losses. - Works on monocular sequences at scale. **Hybrid Refinement**: - Fuse geometric priors with neural depth prediction. - Improves robustness in low-texture regions. **How It Works** **Step 1**: - Estimate inter-frame motion and correspondences from video. **Step 2**: - Solve depth through geometric triangulation or train depth model with temporal photometric consistency. Depth from video is **a core geometric inference task that turns temporal motion cues into actionable 3D scene understanding** - reliable depth estimation enables richer perception and control in many vision systems.