Saliency Maps for Point Clouds

Figueiredo, Victor F.; Sandri, Gustavo; Queiroz, Ricardo L. de; Chou, Philip A.

doi:10.1109/mmsp48831.2020.9287102

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Beyond 360-degree content, volumetric images and video have been under deep investigation from the users’ behavior perspective. Saliency for point clouds has been studied in [ 186 , 187 , 188 ], and machine learning tools can play a key role in this very recent research direction.…”

Section: Learning-based Transmissionmentioning

confidence: 99%

Machine Learning for Multimedia Communications

Thomos

Maugey

Toni

2022

Sensors

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Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

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Section: Learning-based Transmissionmentioning

confidence: 99%

Machine Learning for Multimedia Communications

Thomos

Maugey

Toni

2022

Sensors

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“…G. Ma, et al revisit the problem of image salient object detection from the semantic perspective, providing a novel network for learning the relative semantic saliency degree for two input object proposals [22]. V. Figueiredo et al [23] utilize orthographic projections in 2D planes and apply established saliency detection algorithms to create a 3D point cloud saliency map. In contrast, X. Ding et al [24] propose a novel approach for detecting saliency in point clouds by jointly utilizing both local distinctness and global rarity cues, which are supported by psychological evidence.…”

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confidence: 99%

TSA based optimization to dc voltage and dc modulation for high-voltage ride-through (HVRT) of DFIG: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Zhao

2023

2023 IEEE 6th International Electrical and Energy Conference (CIEEC)

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Volumetric video, also known as hologram video, is a novel medium that portrays natural content in Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). It is expected to be the next-gen video technology and a prevalent use case for 5G and beyond wireless communication. Considering that each user typically only watches a section of the volumetric video, known as the viewport, it is essential to have precise viewport prediction for optimal performance. However, research on this topic is still in its infancy. In the end, this paper presents and proposes a novel approach, named Saliency and Trajectory Viewport Prediction (STVP), which aims to improve the precision of viewport prediction in volumetric video streaming. The STVP extensively utilizes video saliency information and viewport trajectory. To our knowledge, this is the first comprehensive study of viewport prediction in volumetric video streaming. In particular, we introduce a novel sampling method, Uniform Random Sampling (URS), to reduce computational complexity while still preserving video features in an efficient manner. Then we present a saliency detection technique that incorporates both spatial and temporal information for detecting static, dynamic geometric, and color salient regions. Finally, we intelligently fuse saliency and trajectory information to achieve more accurate viewport prediction. We conduct extensive simulations to evaluate the effectiveness of our proposed viewport prediction methods using state-of-the-art volumetric video sequences. The experimental results show the superiority of the proposed method over existing schemes. The dataset and source code will be publicly accessible after acceptance.

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Embedded Bit-Stream Region-of-Interest Coding of Point Cloud Attributes

Figueiredo,

de Queiroz

2024

2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)

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Saliency Maps for Point Clouds

Cited by 5 publications

References 13 publications

Machine Learning for Multimedia Communications

Machine Learning for Multimedia Communications

TSA based optimization to dc voltage and dc modulation for high-voltage ride-through (HVRT) of DFIG: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Embedded Bit-Stream Region-of-Interest Coding of Point Cloud Attributes

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