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

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"
}