How to Perform Monocular 360 Depth Estimation using BiFuse

Welcome to our guide on utilizing the revolutionary BiFuse technique introduced in the CVPR 2020 paper! This innovative method allows for depth estimation from a single RGB image by cleverly fusing projections from multiple perspectives.

Getting Started

Before diving into the usage, let’s make sure you have everything set up correctly. Here are the requirements:

• Python (tested on 3.7.4)
• PyTorch (tested on 1.4.0)
• Other dependencies installed via command:
• pip install -r requirements.txt

Step-by-Step Usage

Follow these steps to start using the BiFuse tool:

Step 1: Clone the Repository

First, clone the BiFuse repository using Git:

git clone https://github.com/Yeh-yu-hsuan/BiFuse.git
cd BiFuse

Step 2: Download the Pretrained Model

Next, download the pretrained model file from Google Drive. Create a save folder in your BiFuse directory and place the downloaded file in there:

mkdir save
# Move the model to the save folder
# (Assuming you downloaded it to the current directory)
mv BiFuse_Pretrained.pkl save/

Step 3: Prepare Your Test Data

The My_Test_Data folder already contains a sample image (Sample.jpg) for testing. If you want to use your own images, make sure to place them in the My_Test_Data folder. Now run the following command:

python main.py --path My_Test_Data

In this command:

• –path: Specifies the folder containing your RGB images.
• –nocrop: Use this option if you prefer not to crop the original images.

Step 4: View the Results

Once the script finishes executing, you can find the results in the My_Test_Result folder. The outputs include:

• Combine.jpg: A combination of the RGB image and its depth map prediction.
• Depth.jpg: Shows the depth map prediction alone.
• Data.npy: Contains the original RGB data alongside the predicted depth values.

Point Cloud Visualization

If you wish to visualize the point cloud of the predicted depth, a script is available. You can check it out in the tools directory.

Troubleshooting

If you encounter any issues while setting up or running the program, here are some troubleshooting tips:

• Ensure all dependencies are correctly installed using the command provided in the requirements section.
• Verify that your downloaded model file is placed in the correct save folder.
• Check that your image files in My_Test_Data are in the correct format (RGB images).
• For file path issues, double-check the paths provided in your commands.
• If you need further assistance or insights, please feel free to reach out. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.