MegaDepth: Learning Single-View Depth Prediction from Internet Photos

Zhengqi Li    Noah Snavely

Cornell University/Cornell Tech

In CVPR, 2018

We use large Internet image collections, combined with 3D reconstruction and semantic labeling methods, to generate large amounts of training data for single-view depth prediction. (a), (b), (e): Example input RGB images. (c), (d), (f): Depth maps predicted by our MegaDepth-trained CNN (blue=near, red=far). For these results, the network was not trained on Make3D and KITTI data.


Single-view depth prediction is a fundamental problem in computer vision. Recently, deep learning methods have led to significant progress, but such methods are limited by the available training data. Current datasets based on 3D sensors have key limitations, including indoor-only images (NYU), small numbers of training examples (Make3D), and sparse sampling (KITTI). We propose to use multi-view Internet photo collections, a virtually unlimited data source, to generate training data via modern structure-from-motion and multi-view stereo (MVS) methods, and present a large depth dataset called MegaDepth based on this idea. Data derived from MVS comes with its own challenges, including noise and unreconstructable objects. We address these challenges with new data cleaning methods, as well as automatically augmenting our data with ordinal depth relations generated using semantic segmentation. We validate the use of large amounts of Internet data by showing that models trained on MegaDepth exhibit strong generalization---not only to novel scenes, but also to other diverse datasets including Make3D, KITTI, and DIW, even when no images from those datasets are seen during training.

Examples of Single-View Depth Predictions on Internet Photos

paper thumbnail


Zhengqi Li and Noah Snavely. "MegaDepth: Learning Single-View Depth Prediction from Internet Photos".

  title={MegaDepth: Learning Single-View Depth Prediction from Internet Photos},
  author={Zhengqi Li and Noah Snavely},
  booktitle={Computer Vision and Pattern Recognition (CVPR)},

MegaDepth v1 Dataset

The MegaDepth dataset includes 196 different locations reconstructed from COLMAP SfM/MVS. (Update images/depth maps with original resolutions generated from COLMAP MVS)

MegaDepth v1 Dataset (tar.gz, 199 GB)
MegaDepth v1 Dataset README

MegaDepth v1 SfM models

We also provide SfM models for all the 196 locations around world, and every model includes SIFT features locations, sparse 3D points clouds as well as camera intrinisics/extrinsics in both COLMAP and Bundler format.

MegaDepth SfM models (tar.xz, 667 GB)
MegaDepth v1 Dataset README

MegaDepth demo code for post-processing MVS depths

As requested by several people, we provide a simple demo code for getting MVS depths from COLMAP with significantly less outliers, which can be used for training networks (written in Matlab).

Matlab demo post-processing code

MegaDepth training/validation sets list


MegaDepth test set list




Simple demo training code

As requested, we provide a simple demo training code. Note that this is not fully working code but should give you basic ideas of training the networks on our MegaDepth dataset.


Pretrained Models

NOTE: We found some evaluation issues in our paper that we didn't realized before, we are fixing them and we will update our paper in ArXiv and models very soon. Sorry for any inconveience.

Live Demo

Live Demo Now Online!


We thank the anonymous reviewers for their valuable comments. This work was funded by the National Science Foundation under grant IIS-1149393.