PanoFusion: A Monocular Omnidirectional Depth Estimation Model

Akashah Shabbir, Dmitry Demidov, Mohammad Ibraheem Siddiqui, Sara Ghaboura

Introduction

The PanoFusion model is inspired by two state-of-the-art works: the PanoDepth repository and OmniFusion repository, both authored by created by Yuyan Li. Building on these models, we developed PanoFusion, a model that estimates depth in 360-degree images in two stages. First, it makes a basic depth estimate using OmniFusion model at stage 1, hence getting enhanced synthesized views serving as refined input for the PanoDepth stage 2, the stereo matching phase. This leads to a much-improved final depth estimation. Compared to the original two models, PanoFusion shows better performance.

Pipeline

Figure 1. The 2-stage PanoFusion Framework. Stage 1: the pipeline accepts an ERP as input and synthesizes novel views (SV) based on the OmniFusion model for the next stage. Stage 2: the model takes the original ERP input image and the SV for the stereo-matching phase based on the Panodepth model. The result is a refined depth estimation.

Examples:

I- Images:

Stanford2D3D dataset example:

Figure 2. Left: Stanford2D3D ERP image as input to PanoFusion. Center: The depth estimation (DE) as output. Right: The ERP and the DE as input to MeshLab, the result is a 3D point cloud.

MBZUAI MetaverseLab example:

Figure 3. Left: MBZUAI MetaverseLab ERP image as input to PanoFusion. Center: The depth estimation (DE) as output. Right: The ERP and the DE as input to MeshLab, the result is a 3D point cloud.

II- Videos:

Figure 4. Left: Stanford2D3D example 3D point cloud Demo.. Right: MBZUAI MetaverseLab example 3D point cloud Demo.

Instructions

Prepare the environment:

cd <repo>

conda create -n omnifusion_pip python=3.7.4 
conda activate omnifusion_pip
pip3 install -r requirements.txt
conda install -c conda-forge openexr-python # yes
pip install mmcv==1.5.0
pip install mmsegmentation==0.23.0

Run:

cd <repo>

conda activate omnifusion_pip

python3 main_fullpipeline.py # fine-tuning

python3 evaluate_fullpipeline.py # use on test dataset

python3 evaluate_fullpipeline_mymg.py # use on random images

Models

Other

cd '/l/users/20020067/Study/PhD/2 semester/CV-802 (3D vision)/Project/_Ours/PanoFusion/'

source /apps/local/anaconda2023/conda_init.sh
# (local):
conda activate omnifusion_pip
# (shared): 
conda activate /l/users/MODE/envs/omnifusion_pip

python ./OmniFusion/test.py --nrows 4

Create environment:

conda activate omnifusion_pip
pip3 install -r requirements.txt
#conda install -c conda-forge openexr # not rly?
conda install -c conda-forge openexr-python # yes
pip install mmcv==1.5.0
pip install mmsegmentation==0.23.0

copy environment to the shared folder:

conda create --prefix ./name --clone name

Dataset

We trained our method on Stanford2D3D.
We evaluate our model on Stanford2D3D and our own MBZUAI-MetaverseLab ERP images.

Notes:

For Stanford2D3D: a standard pre-process of the original Matterport3D dataset is applied for the development of 360-image-based depth estimation.
For MBZUAI-MetaverseLab: images were captured on the MetaverseLab environment, with an Insta360 X3 camera.

demidovd98/PanoFusion