The DeepLIDAR Home Remodeling Platform is a project that explores the application of deep learning techniques to LIDAR sensor data, particularly in the context of home remodeling and interior design. This platform aims to leverage the advancements in 3D detection technologies, making 3D sensors like LIDAR more accessible and affordable. The system combines 3D data from LIDAR sensors with 2D images to provide machines with a comprehensive understanding of their surroundings.

• Deep Learning Integration: The platform utilizes state-of-the-art deep learning techniques to process LIDAR data. This involves overcoming unique challenges associated with 3D datasets, such as point clouds, using complex neural networks.

• Real-Time Inference: The system is designed to perform real-time inference, demonstrating the feasibility of applying deep learning to LIDAR data in practical scenarios.

• Focus on Home Remodeling and Interior Design: The project specifically targets the domain of home remodeling and interior design. This includes applications such as room layout optimization, furniture placement, and virtual remodeling.

• Swift-based Frontend: The frontend of the application is developed in Swift, providing a user-friendly interface for customers to interact with the platform.

• Firebase Backend and Database: The backend infrastructure and database are deployed on Firebase, ensuring scalability, real-time updates, and seamless communication between the frontend and backend components.

• Xcode: Ensure that you have Xcode installed on your development machine to work with the Swift-based frontend.

1. Clone the repository:

   git clone https://github.com/aaryaneil/Home-Remodel.git
   cd Home-Remodel

2. Open the Swift project in Xcode.

3. Connect the frontend to Firebase:

   • Set up a Firebase project and obtain the necessary configuration files.
   • Integrate Firebase SDK into the Xcode project.

4. Run the application on a simulator or a physical device.