A new fast filtering algorithm for a 3D point cloud based on RGB-D information

Jia, Chaochuan; Yang, Ting; Wang, Chuanjiang; Fan, Bin; He, Fenghua

doi:10.1371/journal.pone.0220253

Cited by 16 publications

(11 citation statements)

References 36 publications

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“…As described in Reference [ 18 ], the SOR filter perceives points at borders as outlying points. These filters also remove the small areas (clusters of points that are not interference points) [ 27 , 28 ]. As described in Reference [ 20 ], SOR filtering is not entirely appropriate for real-time performance.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Interference Artifacts Suppression in Array of ToF Sensors

Volák

Bajzík

Janisova

et al. 2020

Sensors

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Time of Flight (ToF) sensors are the source of various errors, including the multi-camera interference artifact caused by the parallel scanning mode of the sensors. This paper presents the novel Importance Map Based Median filtration algorithm for interference artifacts suppression, as the potential 3D filtration method. The approach is based on the processing of multiple depth frames, using the extraction of the interference region and application of the interpolation. Considering the limitations and good functionalities of proposed algorithm, the combination with some standard methods was suggested. Performance of the algorithm was evaluated on the dataset consisting of the real-world objects with different texture and morphology against popular filtering methods based on neural networks and statistics.

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Section: Discussionmentioning

confidence: 99%

Real-Time Interference Artifacts Suppression in Array of ToF Sensors

Volák

Bajzík

Janisova

et al. 2020

Sensors

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“…Closest to our ToFClean approach is the approach in [ 45 ], which makes use of depth cameras with RGB data as well. We can also make use of IR data from our custom depth camera, but the algorithm itself functions with depth data as well.…”

Section: Background and Methodsmentioning

confidence: 99%

Feature Pyramid Network Based Efficient Normal Estimation and Filtering for Time-of-Flight Depth Cameras

Molnár

Kelényi

Tamás

2021

Sensors

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In this paper, an efficient normal estimation and filtering method for depth images acquired by Time-of-Flight (ToF) cameras is proposed. The method is based on a common feature pyramid networks (FPN) architecture. The normal estimation method is called ToFNest, and the filtering method ToFClean. Both of these low-level 3D point cloud processing methods start from the 2D depth images, projecting the measured data into the 3D space and computing a task-specific loss function. Despite the simplicity, the methods prove to be efficient in terms of robustness and runtime. In order to validate the methods, extensive evaluations on public and custom datasets were performed. Compared with the state-of-the-art methods, the ToFNest and ToFClean algorithms are faster by an order of magnitude without losing precision on public datasets.

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“…PCs obtained with RGB-D sensors generate noise depending on the texture of the object surface, lighting condition, viewing angle, sensor restriction and distance to object. Therefore, filters such as Kalman are adapted to the sensors or the PC generated from the depth map is filtered to remove potential noise (Jia et al 2019). In this paper, a Kinect 2 RGB-D sensor is used.…”

Section: Rgb-d Sensormentioning

confidence: 99%

Comparison of point cloud filtering methods with data acquired by photogrammetric method and RGB-D sensors

Günen¹

2021

International Journal of Engineering and Geosciences

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Point clouds (PCs) are inevitable sources to generate digital solid model-based applications such as reverse engineering, differential 3D modelling, 3D sensing and modelling of environments, scene reconstruction, augmented reality. Photogrammetric methods, Terrestrial Laser Scanners and RGB-D sensors are relatively common among the technologies used to capture PCs. Because of their structural characteristics, measuring systems produce large amounts of noise that cannot be precisely predicted in type and amplitude. Due to the noisy measurements, the spatial orientations of the differential surface particles and the spatial locations of the corner points have a certain degree of deformation. In order to increase visual, spatial and physical quality of the solid model, which is frequently used in reverse engineering, PCs must be filtered to discard noise and outlier. In this paper PC produced from different methods was filtering with Shepard Inverse Distance Weighting method, Gaussian Filtering method, Single Value Decomposition Based Plane Fitting method and Optimization Based Plane Fitting method. Backtracking Search Optimization Algorithm (BSA) was used to fitting plane. Experimental results were compared visually and statistical according to the number of neighborhoods. The results showed that Backtracking Search Optimization based filtering supplied better noise smoothing results than its competitors.

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A new fast filtering algorithm for a 3D point cloud based on RGB-D information

Cited by 16 publications

References 36 publications

Real-Time Interference Artifacts Suppression in Array of ToF Sensors

Real-Time Interference Artifacts Suppression in Array of ToF Sensors

Feature Pyramid Network Based Efficient Normal Estimation and Filtering for Time-of-Flight Depth Cameras

Comparison of point cloud filtering methods with data acquired by photogrammetric method and RGB-D sensors

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