Detection of 3D points on moving objects from point cloud data for 3D modeling of outdoor environments

Kanatani, Tsunetake; Kume, Hideyuki; Taketomi, Takafumi; Sato, Tomokazu; Yokoya, Naokazu

doi:10.1109/icip.2013.6738446

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…When outdoor environmental modeling [ 39 ] is carried out with lidar, point cloud noise is generated at the stage of obtaining PCD. The aforementioned methods can effectively remove discrete outlier points which are treated as a noisy target.…”

Section: Introductionmentioning

confidence: 99%

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Cheng

Zhao

Zhang

et al. 2021

Sensors

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Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

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Section: Introductionmentioning

confidence: 99%

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Cheng

Zhao

Zhang

et al. 2021

Sensors

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“…Kanatani et al [7] detect 3D points on moving objects using photometric consistency between pixels obtained by projecting a 3D point onto omnidirectional images captured from different viewpoints. Since this method uses only photometric consistency, this method cannot detect points on moving objects whose luminance values are similar to those on background static objects.…”

Section: Introductionmentioning

confidence: 99%

Moving object detection from a point cloud using photometric and depth consistencies

Atsushi

Takehara

Kawai

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-3D models of outdoor environments have been used for several applications such as a virtual earth system and a vision-based vehicle safety system. 3D data for constructing such 3D models are often measured by an on-vehicle system equipped with laser rangefinders, cameras, and GPS/IMU. However, 3D data of moving objects on streets lead to inaccurate 3D models when modeling outdoor environments. To solve this problem, this paper proposes a moving object detection method for point clouds by minimizing an energy function based on photometric and depth consistencies assuming that input data consist of synchronized point clouds, images, and camera poses from a single sequence captured with a moving on-vehicle system.

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