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# System prepended metadata

title: Project definition

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# Project definition

### Roles
Sponsor/Stakeholder: TU Delft
Product owner: Our supervisor
Scrum master: Alexander Freeman
Team members: Alessandro Duico, Boris Berman

### Product
##### Input
Study potential of NeRF
Study efficiency improvements
##### Output
Extension of the base NeRF/BlockNeRF that can work with the provided pictures taken from a car

### User stories

### Blacklog
As a service provider, I should be able to create a reconstructions of real places and roads using images taken from a moving car without any moving object.
As a service provider, I should be able to create reconstructions of real places and roads using images taken from a moving car, with traffic and moving objects.
As a service provider, I should be able to generate reconstructions in a computationally efficient way.

As a user, I should be able to visualize reconstructions of real places online.
As a user, I should be able to see noiseless and crisp reconstructions.
As a user, I should be able to move the camera smoothly.

### MOSCOW (unrelated)

Must understand the concepts of the NeRF methods

Must become familiar with the recent NeRF papers

Should reproduce the paper results of one or more NeRF methods on benchmark data

Should apply one or more NeRF methods to images from an outdoor driving scene (a street)

Should analyse the NeRF synthesis results and artifacts in outdoor driving environments e.g. how well does the method cope with limited motion (predominantly longitudinal), or dynamic traffic situations?

Could develop a more data- and computation-efficient NeRF algorithm

Could develop a NeRF algorithm, which can control some appearance conditions of the synthesis environment, such as illumination and weather

Won’t realize large-scale 3D reconstruction of a full block- or city-level


### Planning

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### Way of working

Online meetings: Zoom
Scrum board: **!TODO** pick one
File-sharing: **!TODO** pick one
Chat: Telegram
Off-line communication: Telegram