Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod R.2
Authors: Palazzolo, Emanuele; Stachniss, Cyrill
Title: Fast Image-Based Geometric Change Detection Given a 3D Model
Abstract:
3D models of the environment are used in numerous robotic applications and should reflect the current state of the world. In this paper, we address the problem of quickly finding structural changes between the current state of the world and a given 3D model using a small number of images. Our approach finds inconsistencies between pairs of images by re-projecting an image onto another one by passing through the given 3D model. This process leads to ambiguities, which we resolve by combining multiple images such that the 3D location of the change can be estimated. A focus of our approach is that it can be executed fast enough to allow the operation on a mobile system. We implemented our approach in C++ and released it as open source software. We tested it on existing datasets as well as on self-recorded image sequences and 3D models, which we publicly share. Our experiments show that our method quickly finds changes in the geometry of a scene.
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod R.2
Authors: Palazzolo, Emanuele; Stachniss, Cyrill
Title: Fast Image-Based Geometric Change Detection Given a 3D Model
Abstract:
3D models of the environment are used in numerous robotic applications and should reflect the current state of the world. In this paper, we address the problem of quickly finding structural changes between the current state of the world and a given 3D model using a small number of images. Our approach finds inconsistencies between pairs of images by re-projecting an image onto another one by passing through the given 3D model. This process leads to ambiguities, which we resolve by combining multiple images such that the 3D location of the change can be estimated. A focus of our approach is that it can be executed fast enough to allow the operation on a mobile system. We implemented our approach in C++ and released it as open source software. We tested it on existing datasets as well as on self-recorded image sequences and 3D models, which we publicly share. Our experiments show that our method quickly finds changes in the geometry of a scene.
