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

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"
}