D-LaMa: Depth Inpainting of Perspective-Occluded Environments

Kurzfassung

Occlusion is a common problem in computer vision where backgrounds or objects are occluded by other objects in the foreground. Occlusion affects object recognition or tracking and influences scene understanding with the associated depth estimation and spatial perception. To solve the associated problems and improve the detection of areas, we propose a pre-trained image distortion model that allows us to incorporate new perspectives within previously rendered point clouds. We investigate approaches in synthetically generated use cases: Masking previously generated virtual images and depth images, removing and painting over a provided mask, and the removal of objects from the scene. Our experimental results allow us to gain valuable insights into fundamental problems of occlusion configurations and confirm the effectiveness of our approaches. Our research findings serve as a guide to applying our model to real-life scenarios and ultimately solve the occlusion problem.