Fast Image-Based Geometric Change Detection Given a 3D Model

Palazzolo, Emanuele; Stachniss, Cyrill

doi:10.1109/icra.2018.8461019

Cited by 35 publications

(43 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods rely on an offline processing of the images and are still too expensive to use on simple devices. In [34], a faster approach is proposed for the purpose of the autonomous exploration of an environment by a robot. Although the technique performs in interactive time, it is strongly biased toward the detection of an object's insertion into a scene rather than its removal.…”

Section: Change Detection With Heterogeneous Datamentioning

confidence: 99%

Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

Roupin¹,

Fradet²,

Baillard³

et al. 2021

Electronics

View full text Add to dashboard Cite

Precise knowledge of the real environment is a prerequisite for the integration of the real and virtual worlds in mixed-reality applications. However, real-time updating of a real environment model is a costly and difficult process; therefore, hybrid approaches have been developed: An updated world model can be inferred from an offline acquisition of the 3D world, which is then updated online using live image sequences under the condition of developing fast and robust change detection algorithms. Current algorithms are biased toward object insertion and often fail in object removal detection; in an environment where there is uniformity in the background—in color and intensity—the disappearances of foreground objects between the 3D scan of a scene and the capture of several new pictures of said scene are difficult to detect. The novelty of our approach is that we circumvent this issue by focusing on areas of least change in parts of the scene that should be occluded by the foreground. Through experimentation on realistic datasets, we show that this approach results in better detection and localization of removed objects. This technique can be paired with an insertion detection algorithm to provide a complete change detection framework.

show abstract

Section: Change Detection With Heterogeneous Datamentioning

confidence: 99%

Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

Roupin¹,

Fradet²,

Baillard³

et al. 2021

Electronics

View full text Add to dashboard Cite

show abstract

“…First, we generate random scenes with multiple objects in them, which serve as "before" images. Note, that in domains such as satellite imagery [35,54,63] or surveillance/street scenes [1,28,43], typical distractors include Before After GTs: "nothing has changed" "there is no difference" "no change was made"…”

Section: Clevr-change Datasetmentioning

confidence: 99%

Robust Change Captioning

Park

Darrell

Rohrbach

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

111

View full text Add to dashboard Cite

Describing what has changed in a scene can be useful to a user, but only if generated text focuses on what is semantically relevant. It is thus important to distinguish distractors (e.g. a viewpoint change) from relevant changes (e.g. an object has moved). We present a novel Dual Dynamic Attention Model (DUDA) to perform robust Change Captioning. Our model learns to distinguish distractors from semantic changes, localize the changes via Dual Attention over "before" and "after" images, and accurately describe them in natural language via Dynamic Speaker, by adaptively focusing on the necessary visual inputs (e.g. "before" or "after" image). To study the problem in depth, we collect a CLEVR-Change dataset, built off the CLEVR engine, with 5 types of scene changes. We benchmark a number of baselines on our dataset, and systematically study different change types and robustness to distractors. We show the superiority of our DUDA model in terms of both change captioning and localization. We also show that our approach is general, obtaining state-of-the-art results on the recent realistic Spot-the-Diff dataset which has no distractors.

show abstract

“…Spatial change detection is a critical problem in some robotic applications [10][11][12][13][14][15][16]. Andreasson et al [10] proposed autonomous change detection for a security patrol robot.…”

Section: Related Researchmentioning

confidence: 99%

“…Moreover, it considers the confidence about the cell values as opposed to occupancy maps or a most-likely maps. [16]. Palazzolo et al [16] proposed a fast spacial change detection technique using a 3D model and a small number of 2D images.…”

Section: Related Researchmentioning

confidence: 99%

See 1 more Smart Citation

Spatial change detection using normal distributions transform

et al. 2019

View full text Add to dashboard Cite

Spatial change detection is a fundamental technique for finding the differences between two or more pieces of geometrical information. This technique is critical in some robotic applications, such as search and rescue, security, and surveillance. In these applications, it is desirable to find the differences quickly and robustly. The present paper proposes a fast and robust spatial change detection technique for a mobile robot using an on-board range sensors and a highly precise 3D map created by a 3D laser scanner. This technique first converts point clouds in a map and measured data to grid data (ND voxels) using normal distributions transform. The voxels in the map and the measured data are then compared according to the features of the ND voxels. Three techniques are introduced to make the proposed system robust for noise, that is, classification of point distribution, overlapping of voxels, and voting using consecutive sensing. The present paper shows the results of indoor and outdoor experiments using an RGB-D camera and an omni-directional laser scanner mounted on a mobile robot to confirm the performance of the proposed technique.

show abstract

Fast Image-Based Geometric Change Detection Given a 3D Model

Cited by 35 publications

References 8 publications

Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

Robust Change Captioning

Spatial change detection using normal distributions transform

Contact Info

Product

Resources

About