Depth Estimation

Class: DepthEstimationBlockV1

Source: inference.core.workflows.core_steps.models.foundation.depth_estimation.v1.DepthEstimationBlockV1

        🎯 This workflow block performs depth estimation on images using Apple's DepthPro model. It analyzes the spatial relationships
        and depth information in images to create a depth map where:

        📊 Each pixel's value represents its relative distance from the camera
        🔍 Lower values (darker colors) indicate closer objects
        🔭 Higher values (lighter colors) indicate further objects

        The model outputs:
        1. 🗺️ A depth map showing the relative distances of objects in the scene
        2. 📐 The camera's field of view (in degrees)
        3. 🔬 The camera's focal length

        This is particularly useful for:
        - 🏗️ Understanding 3D structure from 2D images
        - 🎨 Creating depth-aware visualizations
        - 📏 Analyzing spatial relationships in scenes
        - 🕶️ Applications in augmented reality and 3D reconstruction

        ⚡ The model runs efficiently on Apple Silicon (M1-M4) using Metal Performance Shaders (MPS) for accelerated inference.

Type identifier

Use the following identifier in step "type" field: roboflow_core/depth_estimation@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
model_version	str	The Depth Estimation model to be used for inference..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Depth Estimation in version v1.

• inputs: Camera Calibration, Image Contours, Crop Visualization, Stability AI Inpainting, Polygon Zone Visualization, Image Convert Grayscale, Image Slicer, Camera Focus, Image Slicer, Circle Visualization, SIFT, Mask Visualization, Reference Path Visualization, Depth Estimation, Grid Visualization, Color Visualization, Line Counter Visualization, Background Color Visualization, Ellipse Visualization, Stability AI Image Generation, Polygon Visualization, Stitch Images, Absolute Static Crop, Trace Visualization, Label Visualization, Triangle Visualization, Dynamic Crop, Perspective Correction, Keypoint Visualization, Halo Visualization, SIFT Comparison, Pixelate Visualization, Image Blur, Blur Visualization, Bounding Box Visualization, Dot Visualization, Image Preprocessing, Classification Label Visualization, Model Comparison Visualization, Corner Visualization, Relative Static Crop, Image Threshold
• outputs: Multi-Label Classification Model, Barcode Detection, Image Contours, Stability AI Inpainting, Keypoint Detection Model, SmolVLM2, Image Convert Grayscale, Image Slicer, LMM For Classification, CogVLM, Multi-Label Classification Model, Pixel Color Count, LMM, Qwen2.5-VL, Circle Visualization, SIFT, OpenAI, VLM as Classifier, Single-Label Classification Model, Reference Path Visualization, Depth Estimation, Line Counter Visualization, Polygon Visualization, VLM as Detector, QR Code Detection, Stitch Images, Absolute Static Crop, Triangle Visualization, Detections Stitch, Time in Zone, Keypoint Visualization, Object Detection Model, VLM as Detector, SIFT Comparison, Bounding Box Visualization, Dominant Color, Image Preprocessing, Detections Stabilizer, Classification Label Visualization, Camera Calibration, Corner Visualization, Relative Static Crop, YOLO-World Model, Image Threshold, Roboflow Dataset Upload, Anthropic Claude, Segment Anything 2 Model, Crop Visualization, Byte Tracker, Polygon Zone Visualization, Florence-2 Model, Single-Label Classification Model, Roboflow Dataset Upload, Instance Segmentation Model, Template Matching, Camera Focus, Image Slicer, Florence-2 Model, SIFT Comparison, Object Detection Model, Mask Visualization, OCR Model, Color Visualization, Google Gemini, Background Color Visualization, Instance Segmentation Model, Llama 3.2 Vision, Ellipse Visualization, Stability AI Image Generation, Gaze Detection, Keypoint Detection Model, OpenAI, Buffer, Trace Visualization, Label Visualization, Dynamic Crop, Perspective Correction, CLIP Embedding Model, Halo Visualization, Pixelate Visualization, Moondream2, VLM as Classifier, Image Blur, Dot Visualization, Blur Visualization, Clip Comparison, Model Comparison Visualization, Clip Comparison, Google Vision OCR

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Depth Estimation in version v1 has.

Bindings

• input

  • images (image): The image to infer on..

• output

  • image (image): Image in workflows.
  • normalized_depth (numpy_array): Numpy array.

Example JSON definition of step Depth Estimation in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/depth_estimation@v1",
    "images": "$inputs.image",
    "model_version": "depth-anything-v2/small"
}