Depth Estimation
Estimate depth and 3D structure from monocular images or stereo pairs, enabling spatial understanding without dedicated depth sensors.
Discuss Your ProjectUse Cases
- Autonomous navigation
- Augmented reality
- 3D photography
- Robotic manipulation
- Volume measurement
- Accessibility applications
Overview
Depth estimation recovers the 3D structure of a scene from 2D images, outputting per-pixel depth values or 3D point clouds. This enables spatial reasoning, obstacle avoidance, and 3D reconstruction from standard cameras.
We implement monocular depth estimation (single image), stereo matching (image pairs), and multi-view depth (video sequences). Modern models like Depth Anything and MiDaS provide remarkably accurate depth from single images, while stereo methods achieve centimeter accuracy.
Depth estimation is crucial for robotics, AR/VR, autonomous vehicles, and any application requiring 3D understanding. We optimize for metric accuracy when ground truth depth is available or relative depth for general scene understanding.
Capabilities
What we can achieve with depth estimation
Monocular Depth Estimation
Infer depth from a single RGB image using deep learning models trained on diverse datasets, ideal for retrofitting depth to existing cameras.
Stereo Depth Matching
Compute accurate depth from stereo camera pairs by matching corresponding points across left and right images.
Metric Depth Estimation
Estimate absolute depth values in real-world units (meters) rather than relative depth, essential for measurement applications.
Video Depth & Consistency
Temporally consistent depth estimation across video frames, avoiding flickering and maintaining 3D coherence.
3D Reconstruction
Build dense 3D point clouds and meshes from depth estimates for digital twins, mapping, and visualization.
Technologies We Use
Industries We Serve
This solution is applicable across multiple industries where visual data analysis is critical.
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