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

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"
}