With the increasing availability of egocentric wearable devices, there has been a surge in first-person videos, leading to numerous studies aiming to leverage this data. Among these efforts, 3D scene reconstruction stands out as a key area of interest. This process allows for the recreation of the scene where the video was captured, providing invaluable support for the growing field of augmented reality applications. Some egocentric datasets include static 3D scans of recording locations, usually requiring costly hardware or dedicated scans. An alternative approach involves reconstructing the scene directly from video frames using Structure from Motion (SfM) techniques. This method not only captures the motion of the actor and the objects they interact with, including transformations (e.g., slicing a carrot) but also enables the use of any egocentric footage for scene reconstruction, even without physical access to the environment in real life.