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

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"
}