Dynamic 3D Scene Reconstruction and Enhancement

2017 International Conference on 3D Vision (3DV)

Fougerolle

et al. 2017

Self Cite

In the context of scene modelling, understanding, and landmark-based robot navigation, the knowledge of static scene parts and moving objects with their motion behaviours plays a vital role. We present a complete framework to detect and extract the moving objects to reconstruct a high quality static map. For a moving 3D camera setup, we propose a novel 3D Flow Field Analysis approach which accurately detects the moving objects using only 3D point cloud information. Further, we introduce a Sparse Flow Clustering approach to effectively and robustly group the motion flow vectors. Experiments show that the proposed Flow Field Analysis algorithm and Sparse Flow Clustering approach are highly effective for motion detection and segmentation, and yield high quality reconstructed static maps as well as rigidly moving objects of real-world scenarios.

Section: Static Map and Rigid Object Reconstructionmentioning

confidence: 99%

Static and Dynamic Objects Analysis as a 3D Vector Field

2017 International Conference on 3D Vision (3DV)

Fougerolle

et al. 2017

Self Cite

“…The 3-point RANSAC algorithm uses Cayley representation of the rotation matrix, which allows to obtain rigid transformation between point clouds using linear solvers, similar to [6]. In addition, an Iterative Closest Point algorithm [7] is applied to refine the registration. Finally, the reconstruction of the moving objects is also obtained, in a very similar manner -by registering their observations from different view-ports.…”

Section: Static-map and Dynamic Object Reconstructionmentioning

confidence: 99%

Incomplete 3D motion trajectory segmentation and 2D-to-3D label transfer for dynamic scene analysis

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Fougerolle

et al. 2017

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The knowledge of the static scene parts and the moving objects in a dynamic scene plays a vital role for scene modelling, understanding, and landmark-based robot navigation. The key information for these tasks lies on semantic labels of the scene parts and the motion trajectories of the dynamic objects. In this work, we propose a method that segments the 3D feature trajectories based on their motion behaviours, and assigns them semantic labels using 2D-to-3D label transfer. These feature trajectories are constructed by using the proposed trajectory recovery algorithm which takes the loss of feature tracking into account. We introduce a complete framework for static-map and dynamic objects' reconstruction, as well as semantic scene understanding for a calibrated and moving 2D-3D camera setup. Our motion segmentation approach is faster by two orders of magnitude, while performing better than the state-of-the-art 3D motion segmentation methods, and successfully handles the previously discarded incomplete trajectory scenarios.

“…Therefore, such reconstruction suffers from several visual artefacts due to the dynamic parts. In this regard, recent work by Jiang et al [4][5][6] categorizes the scene into static and dynamic parts before performing VO. Their method focuses on improving VO measurements, and the attempted dynamic object reconstruction is rather preliminary and naive.…”

Section: Introductionmentioning

confidence: 99%

High quality reconstruction of dynamic objects using 2D-3D camera fusion

Christie

2017 IEEE International Conference on Image Processing (ICIP)

et al. 2017

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In this paper, we propose a complete pipeline for high quality reconstruction of dynamic objects using 2D-3D camera setup attached to a moving vehicle. Starting from the segmented motion trajectories of individual objects, we compute their precise motion parameters, register multiple sparse point clouds to increase the density, and develop a smooth and textured surface from the dense (but scattered) point cloud. The success of our method relies on the proposed optimization framework for accurate motion estimation between two sparse point clouds. Our formulation for fusing closest-point and consensus based motion estimations, respectively in the absence and presence of motion trajectories, is the key to obtain such accuracy. Several experiments performed on both synthetic and real (KITTI) datasets show that the proposed framework is very robust and accurate.