Generate depth maps from images. Visually distinguish near and far objects to represent 3D depth perception.
Drag or click to upload image
JPG, PNG, WebPClick the upload area or drag an image to upload. Supports JPG, PNG, and WebP formats.
Adjust color map, sensitivity, and blur amount. Higher sensitivity emphasizes depth differences, higher blur produces smoother results.
Click "Start Depth Estimation" to begin analysis. Brightness, color, and edge information are combined to estimate depth.
Compare original and depth map side by side. Download as PNG if satisfied.
A depth map represents how far each pixel in an image is from the camera. Bright areas indicate closer objects, while dark areas represent distant objects.
It combines luminance analysis, Sobel edge detection, color saturation analysis, and Gaussian blur to estimate depth. Processed in real-time in the browser without deep learning models.
Landscape photos, indoor scenes, and portraits with clear foreground and background separation produce the best results. Solid backgrounds or abstract patterns may yield less accurate results.
Grayscale shows basic black-and-white depth. Rainbow uses spectrum colors. Thermal mimics thermal camera style. Ocean uses blue-green tones to represent depth.
Yes, all processing is done locally in your browser and images are never uploaded to any server. Works even without internet connection.
A depth map is an image that represents how far each pixel is from the camera as a brightness value. Bright pixels indicate nearby objects, while dark pixels represent distant objects. This tool uses a heuristic-based algorithm that combines luminance analysis, Sobel edge detection, color saturation analysis, and vertical position bias to estimate depth. Applying Gaussian blur reduces noise and produces a smoother depth map. All calculations are processed in real time in the browser.
Recent advances in deep learning have made it possible to estimate precise depth even from a single image. AI models such as MiDaS, DPT, and Depth Anything learn from large datasets to understand spatial depth similarly to how humans perceive it. Traditional stereo camera methods use the parallax between two cameras, but AI monocular depth estimation generates high-accuracy depth maps from just a single photo. Combined with LiDAR sensors, even more precise 3D environment perception is possible in autonomous vehicles, drones, and robotics.
Depth maps are used as a core technology across many fields. In computer graphics, they are used for bokeh background blur effects, 3D model generation, and virtual reality (VR) content creation. The portrait mode on smartphone cameras uses depth maps to naturally blur backgrounds. Self-driving cars use depth maps generated from cameras and LiDAR to determine distances to surrounding obstacles. In medicine, depth information is also used for 3D reconstruction of endoscopic images and precise control of surgical robots. In game development, depth maps are essential for converting real photos into 3D environments.