Local Texture and Geometry Descriptors for Fast Block-Based Motion Estimation of Dynamic Voxelized Point Clouds

Dorea, Camilo; Hung, Edson M.; Queiroz, Ricardo L. de

doi:10.1109/icip.2019.8803690

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Based on (13), directly estimating the normal vector for the entire 64×64 blocks would introduce floating point operations. For simplicity, a CU with the size 64×64 is split into 256 nonoverlapped 4×4 blocks.…”

Section: B Implementationmentioning

confidence: 99%

“…Recent works have been concerned with motion estimation (ME) and motion compensation (MC) to reduce temporal redundancy. Specifically, ME and MC are performed on either 3D cubes [10]- [13] or 2D blocks [14]- [17]. However, the point clouds are with irregular partitions such that some points in consecutive static point cloud (SPC) frames may not have explicit correspondences.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Geometry Surface Coding for Dynamic Point Cloud Compression

Xiong

Gao²,

Wang³

et al. 2021

Preprint

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In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected onto 2D images for compressing with the existing video codecs. However, the existing video codecs are originally designed for natural visual signals, and it fails to account for the characteristics of point clouds. Thus, there are still problems in the compression of geometry information generated from the point clouds. Firstly, the distortion model in the existing rate-distortion optimization (RDO) is not consistent with the geometry quality assessment metrics. Secondly, the prediction methods in video codecs fail to account for the fact that the highest depth values of a far layer is greater than or equal to the corresponding lowest depth values of a near layer. This paper proposes an advanced geometry surface coding (AGSC) method for dynamic point clouds (DPC) compression. The proposed method consists of two modules, including an error projection model-based (EPM-based) RDO and an occupancy map-based (OM-based) merge prediction. Firstly, the EPM model is proposed to describe the relationship between the distortion model in the existing video codec and the geometry quality metric. Secondly, the EPM-based RDO method is presented to project the existing distortion model on the plane normal and is simplified to estimate the average normal vectors of coding units (CUs). Finally, we propose the OM-based merge prediction approach, in which the prediction pixels of merge modes are refined based on the occupancy map. Experiments tested on the standard point clouds show that the proposed method achieves an average 9.84% bitrate saving for geometry compression.

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Section: B Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Geometry Surface Coding for Dynamic Point Cloud Compression

Xiong

Gao²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For each block, the best matching block in a reference frame is selected according to specific matching criteria, which can be based purely on geometry, e.g., an ICP-based approach that generates rigid transforms [6], or can use a combination of geometry and color attribute information [7]. Recent work has also focused on block-based motion search speedup, including both efficient search pattern design and search window reduction [8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focus on fractional-voxel motion estimation (FvME) under the assumption that integer-voxel MVs (IvMVs) have already been obtained using an existing integer-voxel motion estimation (IvME) scheme [7,8,9,10]. Specifically, in this paper we use precomputed IvMVs from a public database [13].…”

Section: Introductionmentioning

confidence: 99%

Fractional Motion Estimation for Point Cloud Compression

Hong¹,

Pavéz²,

Ortega³

et al. 2022

Preprint

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Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds. Our proposed block-based fractional-voxel motion estimation scheme takes into account the fundamental differences between point clouds and videos, i.e., the irregularity of the distribution of voxels within a frame and across frames. We show that motion compensation can benefit from the higher resolution reference and more accurate displacements provided by fractional precision. Our proposed scheme significantly outperforms comparable methods that only use integer motion. The proposed scheme can be combined with and add sizeable gains to state-of-the-art systems that use transforms such as Region Adaptive Graph Fourier Transform and Region Adaptive Haar Transform.

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“…Early works [11][12][13]39] on dynamic point cloud attribute compression simply follow the common practice of video compression. Specifically, these methods usually follow a three-step pipeline, including 3D motion estimation and compensation, transform coding, and entropy coding.…”

Section: Introductionmentioning

confidence: 99%

4DAC: Learning Attribute Compression for Dynamic Point Clouds

Fang¹,

Hu²,

Yan³

et al. 2022

Preprint

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With the development of the 3D data acquisition facilities, the increasing scale of acquired 3D point clouds poses a challenge to the existing data compression techniques. Although promising performance has been achieved in static point cloud compression, it remains under-explored and challenging to leverage temporal correlations within a point cloud sequence for effective dynamic point cloud compression. In this paper, we study the attribute (e.g., color) compression of dynamic point clouds and present a learningbased framework, termed 4DAC. To reduce temporal redundancy within data, we first build the 3D motion estimation and motion compensation modules with deep neural networks. Then, the attribute residuals produced by the motion compensation component are encoded by the region adaptive hierarchical transform into residual coefficients. In addition, we also propose a deep conditional entropy model to estimate the probability distribution of the transformed coefficients, by incorporating temporal context from consecutive point clouds and the motion estimation/compensation modules. Finally, the data stream is losslessly entropy coded with the predicted distribution. Extensive experiments on several public datasets demonstrate the superior compression performance of the proposed approach. CCS CONCEPTS• Computing methodologies → Artificial intelligence; Machine learning.

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Local Texture and Geometry Descriptors for Fast Block-Based Motion Estimation of Dynamic Voxelized Point Clouds

Cited by 14 publications

References 9 publications

Advanced Geometry Surface Coding for Dynamic Point Cloud Compression

Advanced Geometry Surface Coding for Dynamic Point Cloud Compression

Fractional Motion Estimation for Point Cloud Compression

4DAC: Learning Attribute Compression for Dynamic Point Clouds

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