Monocular Visual-Inertial Depth Estimation

This repository contains code and models for our paper:

Monocular Visual-Inertial Depth Estimation
Diana Wofk, René Ranftl, Matthias Müller, Vladlen Koltun

For a quick overview of the work you can watch the short talk and teaser on YouTube.

Introduction

We present a visual-inertial depth estimation pipeline that integrates monocular depth estimation and visual-inertial odometry to produce dense depth estimates with metric scale. Our approach consists of three stages: (1) input processing, where RGB and IMU data feed into monocular depth estimation alongside visual-inertial odometry, (2) global scale and shift alignment, where monocular depth estimates are fitted to sparse depth from VIO in a least-squares manner, and (3) learning-based dense scale alignment, where globally-aligned depth is locally realigned using a dense scale map regressed by the ScaleMapLearner (SML). The images at the bottom in the diagram above illustrate a VOID sample being processed through our pipeline; from left to right: the input RGB, ground truth depth, sparse depth from VIO, globally-aligned depth, scale map scaffolding, dense scale map regressed by SML, final depth output.

Setup

1. Setup dependencies:

   conda env create -f environment.yaml
   conda activate vi-depth

2. Pick one or more ScaleMapLearner (SML) models and download the corresponding weights to the weights folder.

   Depth Predictor	SML on VOID 150	SML on VOID 500	SML on VOID 1500
   DPT-BEiT-Large	model	model	model
   DPT-SwinV2-Large	model	model	model
   DPT-Large	model	model	model
   DPT-Hybrid	model*	model	model
   DPT-SwinV2-Tiny	model	model	model
   DPT-LeViT	model	model	model
   MiDaS-small	model	model	model

   *Also available with pretraining on TartanAir: model

Inference

1. Place inputs into the input folder. An input image and corresponding sparse metric depth map are expected:

   input
   ├── image                   # RGB image
   │   ├── <timestamp>.png
   │   └── ...
   └── sparse_depth            # sparse metric depth map
       ├── <timestamp>.png     # as 16b PNG
       └── ...

   The load_sparse_depth function in run.py may need to be modified depending on the format in which sparse depth is stored. By default, the depth storage method used in the VOID dataset is assumed.

2. Run the run.py script as follows:

   DEPTH_PREDICTOR="dpt_beit_large_512"
   NSAMPLES=150
   SML_MODEL_PATH="weights/sml_model.dpredictor.${DEPTH_PREDICTOR}.nsamples.${NSAMPLES}.ckpt"
   
   python run.py -dp $DEPTH_PREDICTOR -ns $NSAMPLES -sm $SML_MODEL_PATH --save-output

3. The --save-output flag enables saving outputs to the output folder. By default, the following outputs will be saved per sample:

   output
   ├── ga_depth                # metric depth map after global alignment
   │   ├── <timestamp>.pfm     # as PFM
   │   ├── <timestamp>.png     # as 16b PNG
   │   └── ...
   └── sml_depth               # metric depth map output by SML
       ├── <timestamp>.pfm     # as PFM
       ├── <timestamp>.png     # as 16b PNG
       └── ...

Evaluation

Models provided in this repo were trained on the VOID dataset.

1. Download the VOID dataset following the instructions in the VOID dataset repo.

2. To evaluate on VOID test sets, run the evaluate.py script as follows:

   DATASET_PATH="/path/to/void_release/"
   
   DEPTH_PREDICTOR="dpt_beit_large_512"
   NSAMPLES=150
   SML_MODEL_PATH="weights/sml_model.dpredictor.${DEPTH_PREDICTOR}.nsamples.${NSAMPLES}.ckpt"
   
   python evaluate.py -ds $DATASET_PATH -dp $DEPTH_PREDICTOR -ns $NSAMPLES -sm $SML_MODEL_PATH

   Results for the example shown above:

   Averaging metrics for globally-aligned depth over 800 samples
   Averaging metrics for SML-aligned depth over 800 samples
   +---------+----------+----------+
   |  metric | GA Only  |  GA+SML  |
   +---------+----------+----------+
   |   RMSE  |  191.36  |  142.85  |
   |   MAE   |  115.84  |   76.95  |
   |  AbsRel |    0.069 |    0.046 |
   |  iRMSE  |   72.70  |   57.13  |
   |   iMAE  |   49.32  |   34.25  |
   | iAbsRel |    0.071 |    0.048 |
   +---------+----------+----------+
   

   To evaluate on VOID test sets at different densities (void_150, void_500, void_1500), change the NSAMPLES argument above accordingly.

Citation

If you reference our work, please consider citing the following:

@inproceedings{wofk2023videpth,
    author      = {{Wofk, Diana and Ranftl, Ren\'{e} and M{\"u}ller, Matthias and Koltun, Vladlen}},
    title       = {{Monocular Visual-Inertial Depth Estimation}},
    booktitle   = {{IEEE International Conference on Robotics and Automation (ICRA)}},
    year        = {{2023}}
}

Acknowledgements

Our work builds on and uses code from MiDaS, timm, and PyTorch Lightning. We'd like to thank the authors for making these libraries and frameworks available.

Last revisited: August 2024