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

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"
}