News

Point Cloud Compression (PCC) has received a lot of attention in recent years due to its wide applications such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Video-based PCC (V-PCC) and geometry-based PCC (G-PCC) are two distinct technologies developed by MPEG 3DG[1][2]. Deep-learning-based (DL-based) PCC is a strong competitor to them. Most DL methods generalize the DL-based image coding pipeline to the point cloud data [3][4]. They outperform G-PCC in the current MPEG 3DG standard in the dense point cloud compression. Yet, their performances are still inferior to that of V-PCC in the coding of dynamic point clouds.

We propose to design a learning-based PCC solution that could outperform those DL-based methods with lower complexity and less memory consumption. Our method uses geometry projection to generate 2D images and apply vector quantization-based 2D image codec to compress the projected map. For a point cloud sequence, we can do the projection in three steps. First, split the sequence into blocks by doing the octree partition. Second, project each 3D block into a plane and pack all the planes into a map. Third, encode/decode the 2D map and reconstruct the 3D point cloud sequence. They are demonstrated in Fig.1. We do the non-uniform sampling for the projected planes and pack all the planes to generate one depth map and one texture map in the reconstruction process. The two maps are shown in Fig.2.

Presently, we utilize the x264/x265 codec to code the maps. In the future, we will adopt a vector quantization-based image codec to compress the two maps.

— Qingyang Zhou

Reference

[1] S. Schwarz, M. Preda, V. Baroncini, M. Budagavi, P. Cesar, P. A. Chou, R. A. Cohen, M. Krivoku ́ca, S. Lasserre, Z. Li et [...]

MCL Director, Professor C.-C. Jay Kuo, has been appointed as the Editor-in-Chief for the APSIPA Transactions on Signal and Information Processing (ATSIP) by the APSIPA Board of Governors. His term starts from January 1, 2022, for two years.
ATSIP was established in 2014. This is the 9th year for the journal. Professor Antonio Ortega of the University of Southern California served as its inaugural EiC from 2014-2017 and Professor Tatsuya Kawahara of Kyoto University was its 2nd EiC from 2018-2021. Professor Kuo expressed his deep gratitude to both Professor Ortega and Professor Kawahara for their contributions in laying out an excellent foundation of the journal. The photo was taken on Dec. 19, 2019, when Professor Kuo and his wife visited Professor Tatsuya Kawahara at Kyoto University.
ATSIP is an open-access e-only journal in partnership with the NOW Publisher. It serves as an international forum for signal and information processing researchers across a broad spectrum of research, ranging from traditional modalities of signal processing to emerging areas where either (i) processing reaches higher semantic levels (e.g., from speech/image recognition to multimodal human behavior recognition) or (ii) processing is meant to extract information from datasets that are not traditionally considered signals (e.g., mining of Internet or sensor information). Papers published in ATSIP are indexed by Scopus, EI and ESCI, searchable on the Web of Science, and included in the IEEE Xplore database.

At the beginning of 2022, We wish all MCL members a more wonderful year with everlasting passion and courage!

Image credit:

https://thenewspocket.com/100-best-happy-new-year-wishes-messages-quotes-2022/

Functional lung imaging is of great importance for the diagnosis and evaluation of lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. Conventional methods often include inhaled hypopolarized gas or 100% oxygen as contrast agents. In recent years, high performance low field systems have shown great advantages for 1H lung MRI due to reduced susceptibility effects and improved vessel conspicuity. These allow possibilities to detect regional volume changes throughout the respiratory cycle.
Recently, under the collabration between MCL and Dynamic Imaging Science Center (DISC), the feasibility of image-based regional lung ventilation assessment from real-time low field MRI at 0.55T is studied, without requiring contrast agents, repetition, or breath holds. A sequence of MRI in the time series with 355ms/frame temporal resolution, 1.64 x 1.64 mm2 spatial resolution, and 15mm slice thickness, captures several consecutive respiratory cycles which consist of different respiratory states from exhalation to inhalation. To resolve the regional lung ventilation based on these acquired images, an unsupervised non-rigid image registration is applied to register the lungs from different respiratory states to the end-of-exhalation. Deformation field is extracted to study the regional ventilation. Specifically, a data-driven binarization algorithm for segmentation is firstly applied to the lung parenchyma area and vessels, separately. A frame-by-frame salient point extraction and matching are performed between the two adjacent frames to form pairs of landmarks. Finally, Jacobian determinant (JD) maps are generated using the calculated deformation fields after a landmark-based B-spline registration.
In the study, the regional lung ventilation is analyzed on three breathing patterns, including free breathing, deep breathing and force exhalation. The motion and volume change for deep breathing and forced exhalation are found to be larger than the free breathing case. [...]

Odometry is the process of using motion sensors to estimate the change in position of an object over time. It has been widely studied in the context of mobile robots, autonomous vehicles, drones, and other moving agents. Traditional odometry based on motion sensors such as Inertial Measurement Unit (IMU) and magnetometers is prone to error accumulation over time, known as odometry drift. Visual odometry makes use of camera images and/or point cloud scans collected over time to determine the position and orientation of the moving object. Several visual odometry systems that integrate monocular, stereo vision, point clouds and IMU have been developed for object localization.
We propose an unsupervised learning method for visual odometry from LiDAR point clouds called Green Point Cloud Odometry (GPCO). GPCO follows the traditional scan matching based approach to solve the odometry problem by incrementally estimating the motion between two consecutive point cloud scans. The GPCO method can be divided into four steps. First, a geometry-aware sampling method selects a small subset of points from the input point clouds. To do so, the eigen features of points in a local neighborhood are considered followed by random point sampling. Next, the 3D view surrounding the moving object is partitioned into four parts representing the front, rear, left and right-side view. The view-partitioning step divides the sampled points into four disjoint sets. The features of the sampled points are derived using the PointHop++ [1] method. Matching points between two consecutive point clouds are found in each view using nearest neighbor rule in the feature space. Finally, the motion between the two scans is estimated using Singular Value Decomposition (SVD). The motion is updated to the estimates from previous times and the process [...]