3D Object Reconstruction using Stationary RGB Camera

3D objects mapping is an important field of computer vision, being applied in games, tracking, and virtual and augmented reality applications. Several techniques implement 3D reconstruction from images obtained by mobile cameras. However, there are situations where it is not possible or convenient to move the acquisition device around the target object, such as when using laptop cameras. Moreover, some techniques do not achieve a good 3D reconstruction when capturing with a stationary camera due to movement differences between the target object and its background. This work proposes two 3D object mapping pipelines from stationary camera images based on COLMAP to solve this type of problem. For that, we modify two background segmentation techniques and motion recognition algorithms to detect foreground without manual intervention or prior knowledge of the target object. Both proposed pipelines were tested with a dataset obtained by a laptop's simple low-resolution stationary ROB camera. The results were evaluated concerning background segmentation and 3D reconstruction of the target object. As a result, the proposed techniques achieve 3D reconstruction results superior to COLMAP, especially in environments with cluttered backgrounds.