Perceptual Optimization for Point-Based Point Cloud Rendering

doi:10.12142/ZTECOM.202304006

Abstract

Abstract:

Point-based rendering is a common method widely used in point cloud rendering. It realizes rendering by turning the points into the base geometry. The critical step in point-based rendering is to set an appropriate rendering radius for the base geometry, usually calculated using the average Euclidean distance of the N nearest neighboring points to the rendered point. This method effectively reduces the appearance of empty spaces between points in rendering. However, it also causes the problem that the rendering radius of outlier points far away from the central region of the point cloud sequence could be large, which impacts the perceptual quality. To solve the above problem, we propose an algorithm for point-based point cloud rendering through outlier detection to optimize the perceptual quality of rendering. The algorithm determines whether the detected points are outliers using a combination of local and global geometric features. For the detected outliers, the minimum radius is used for rendering. We examine the performance of the proposed method in terms of both objective quality and perceptual quality. The experimental results show that the peak signal-to-noise ratio (PSNR) of the point cloud sequences is improved under all geometric quantization, and the PSNR improvement ratio is more evident in dense point clouds. Specifically, the PSNR of the point cloud sequences is improved by 3.6% on average compared with the original algorithm. The proposed method significantly improves the perceptual quality of the rendered point clouds and the results of ablation studies prove the feasibility and effectiveness of the proposed method.

Key words: point cloud rendering, outlier detection, perceptual optimization, point-based rendering, perceptual quality

YIN Yujie, CHEN Zhang. Perceptual Optimization for Point-Based Point Cloud Rendering[J]. ZTE Communications, 2023, 21(4): 47-53.

Figures/Tables 9

Table 1 Point cloud sequence details

Tested Point Clouds	Class (1: people 2: object)	Points	Geometry Precision/bit	Peak Value/bit
Andrew	1	1 276 312	10	1 023
David	1	1 492 780	10	1 023
Phil	1	1 089 091	10	1 023
Ricardo	1	2 592 758	10	1 023
Sarah	1	3 493 085	10	1 023
Head	2	13 903 516	12	4 095
House without roof	2	4 848 745	12	4 095
Egyptian mask	2	272 684	12	4 095
Facade	2	4 061 755	11	2 047
Frog	2	3 614 251	12	4 095

Table 2 PSNR improvement ratio before and after optimization with geometric quantization parameters (PSNR: peak signal-to-noise ratio)

Tested Point Clouds	Average Improvement Ratio of PSNR/%	Geometric Quantization Parameters/%
Tested Point Clouds	Average Improvement Ratio of PSNR/%	1/10	1/2	15/18
Andrew	+3.1	+1.9	+3.2	+4.1
David	+5.1	+2.6	+5.0	+7.6
Phil	+5.2	+2.7	+5.2	+7.7
Ricardo	+3.6	+3.2	+3.6	+4.1
Sarah	+4.4	+2.1	+4.9	+6.2
Facade	+1.5	+1.4	+1.4	+1.7
Head	+4.6	+3.7	+5.0	+5.3
House without roof	+6.0	+3.0	+7.2	+7.9
Egyptian mask	+0.8	+0.7	+0.8	+0.9
Frog	+2.0	+2.1	+2.1	+1.9

Table 3 Ratio of increased rendering time

Tested Point Clouds	Ratio of Increased Rendering Time ( $T r$ )/%
Andrew	6
David	5
Phil	6
Ricardo	5
Sarah	5
Facade	6
Head	8
House without roof	6
Egyptian mask	5
Frog	6
Average	5.8

Table 3 Ratio of increased rendering time

Tested Point Clouds	Ratio of Increased Rendering Time ( $T r$ )/%
Andrew	6
David	5
Phil	6
Ricardo	5
Sarah	5
Facade	6
Head	8
House without roof	6
Egyptian mask	5
Frog	6
Average	5.8

Table 4 Average improvement ratio of PSNR in ablation studies

Tested Point Clouds	PSNR Average Improvement Ratio/%
Tested Point Clouds	Global only	Local only	Global + local
Andrew	+0.6	+1.2	+3.1
David	+0.9	+2.1	+5.1
Phil	+1.0	+2.4	+5.2

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