Easy-to-use user interface:
The project features an intuitive and user-friendly interface,
allowing users to easily navigate and interact with the 3D reconstruction pipeline.
This enables users with varying levels of technical expertise to efficiently utilize the project's functionality.
Built-in 3D geometry viewer:
The project includes a built-in 3D geometry viewer that allows users to visualize and
explore the reconstructed 3D models directly within the application. This feature simplifies
the process of inspecting and analyzing the generated 3D data without the need for external tools or software.
Single input file support:
The project accepts a single mp4 file as input, which can be captured using any phone
or camera. This simplifies the data acquisition process and makes it more accessible to a wide range of users.
Potential applications in various fields:
The project's 3D reconstruction capabilities can be utilized in various fields, such
as computer vision, robotics, cultural heritage preservation, and virtual reality, making it
a valuable tool for a wide range of applications.
Good part:
In contrast to other deep modelling methods, such as differentiable rendering and bag of words, which need a lot of pre-labeled mesh to train, this one doesn't. This pipeline can put back together any shape or scene, even if it doesn't know what the object name is. This is a system that can be used for many different things.
Limitations:
1.This project needs very special hardware like GPU. If a user wants a detailed shape, the training time goes up by a factor of ten.The longer the video, the more details it has. However, every second that the video gets longer, the time it takes to find a match gets longer, and the time it takes to find a match could be anywhere from 1 minute to 3 hours.
2. There is a lot of background noise when shooting the wide. This system can't tell the difference between objects with similar colours. It needs a very different surroundings, like a white background and a red model.
