Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection

Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deployed in another dataset. In this paper, we study the task of training a unified 3D detector from multiple datasets. We observe that this appears to be a challenging task, which is mainly due to that these datasets present substantial data-level differences and taxonomy-level variations caused by different LiDAR types and data acquisition standards. Inspired by such observation, we present a Uni3D which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data-level and taxonomy-level differences, respectively. Our method is simple and easily combined with many 3D object detection baselines such as PV-RCNN and Voxel-RCNN, enabling them to effectively learn from multiple off-the-shelf 3D datasets to obtain more discriminative and generalizable representations. Experiments are conducted on many dataset consolidation settings including Waymo-nuScenes, nuScenes-KITTI, Waymo-KITTI, and Waymo-nuScenes-KITTI consolidations. Their results demonstrate that Uni3D exceeds a series of individual detectors trained on a single dataset, with a 1.04x parameter increase over a selected baseline detector. We expect this work will inspire the research of 3D generalization since it will push the limits of perceptual performance.Comment: Accepted by CVPR-2023, and our code is available at https://github.com/PJLab-ADG/3DTran

AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset

It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks. Previous works mainly focus on the self-supervised pre-training pipeline, meaning that they perform the pre-training and fine-tuning on the same benchmark, which is difficult to attain the performance scalability and cross-dataset application for the pre-training checkpoint. In this paper, for the first time, we are committed to building a large-scale pre-training point-cloud dataset with diverse data distribution, and meanwhile learning generalizable representations from such a diverse pre-training dataset. We formulate the point-cloud pre-training task as a semi-supervised problem, which leverages the few-shot labeled and massive unlabeled point-cloud data to generate the unified backbone representations that can be directly applied to many baseline models and benchmarks, decoupling the AD-related pre-training process and downstream fine-tuning task. During the period of backbone pre-training, by enhancing the scene- and instance-level distribution diversity and exploiting the backbone's ability to learn from unknown instances, we achieve significant performance gains on a series of downstream perception benchmarks including Waymo, nuScenes, and KITTI, under different baseline models like PV-RCNN++, SECOND, CenterPoint.Comment: Code is available at: https://github.com/PJLab-ADG/3DTran

Soil microorganisms and methane emissions in response to short-term warming field incubation in Svalbard

IntroductionGlobal warming is caused by greenhouse gases (GHGs). It has been found that the release of methane (CH4) from Arctic permafrost, soil, ocean, and sediment is closely related to microbial composition and soil factors resulting from warming over several months or years. However, it is unclear for how long continuous warming due to global warming affects the microbial composition and GHG release from soils along Arctic glacial meltwater rivers.MethodsIn this study, the soil upstream of the glacial meltwater river (GR) and the estuary (GR-0) in Svalbard, with strong soil heterogeneity, was subjected to short-term field incubation at 2°C (in situ temperature), 10°C, and 20°C. The incubation was carried out under anoxic conditions and lasted for few days. Bacterial composition and CH4 production potential were determined based on high-throughput sequencing and physiochemical property measurements.ResultsOur results showed no significant differences in bacterial 16S rRNA gene copy number, bacterial composition, and methanogenic potential, as measured by mcrA gene copy number and CH4 concentration, during a 7- and 13-day warming field incubation with increasing temperatures, respectively. The CH4 concentration at the GR site was higher than that at the GR-0 site, while the mcrA gene was lower at the GR site than that at the GR-0 site.DiscussionBased on the warming field incubation, our results indicate that short-term warming, which is measured in days, affects soil microbial composition and CH4 concentration less than the spatial scale, highlighting the importance of warming time in influencing CH4 release from soil. In summary, our research implied that microbial composition and CH4 emissions in soil warming do not increase in the first several days, but site specificity is more important. However, emissions will gradually increase first and then decrease as warming time increases over the long term. These results are important for understanding and exploring the GHG emission fluxes of high-latitude ecosystems under global warming

SPOT: Scalable 3D Pre-training via Occupancy Prediction for Autonomous Driving

Annotating 3D LiDAR point clouds for perception tasks including 3D object detection and LiDAR semantic segmentation is notoriously time-and-energy-consuming. To alleviate the burden from labeling, it is promising to perform large-scale pre-training and fine-tune the pre-trained backbone on different downstream datasets as well as tasks. In this paper, we propose SPOT, namely Scalable Pre-training via Occupancy prediction for learning Transferable 3D representations, and demonstrate its effectiveness on various public datasets with different downstream tasks under the label-efficiency setting. Our contributions are threefold: (1) Occupancy prediction is shown to be promising for learning general representations, which is demonstrated by extensive experiments on plenty of datasets and tasks. (2) SPOT uses beam re-sampling technique for point cloud augmentation and applies class-balancing strategies to overcome the domain gap brought by various LiDAR sensors and annotation strategies in different datasets. (3) Scalable pre-training is observed, that is, the downstream performance across all the experiments gets better with more pre-training data. We believe that our findings can facilitate understanding of LiDAR point clouds and pave the way for future exploration in LiDAR pre-training. Codes and models will be released.Comment: 15 pages, 9 figure

ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation

Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous-domain knowledge can be hardly directly deployed to a new domain without additional costs. In this paper, we provide a new perspective and approach of alleviating the domain shifts, by proposing a Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the implicit reconstruction process is based on the knowledge from the previous old domain, aiming to convert the domain-related knowledge into domain-invariant representations, e.g., 3D scene-level meshes. Besides, the point clouds simulation process of multiple new domains is conditioned on the above reconstructed 3D meshes, where the target-domain-like simulation samples can be obtained, thus reducing the cost of collecting and annotating new-domain data for the subsequent perception process. For experiments, we consider different cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes, Waymo-to-ONCE, etc, to verify the zero-shot target-domain perception using ReSimAD. Results demonstrate that our method is beneficial to boost the domain generalization ability, even promising for 3D pre-training.Comment: Code and simulated points are available at https://github.com/PJLab-ADG/3DTrans#resima

Fabrication of Al2O3/SiC/Al Hybrid Nanocomposites Through Solidification Process for Improved Mechanical Properties

To reduce the cost of nanocomposites and improve the dispersion of nanoparticles, Al2O3np/SiCnp/Al hybrid nanocomposites are fabricated by combining liquid-state blowing and ultrasonic-assisted casting. The average grain size of the matrix decreases to 39 μm in Al2O3np/SiCnp/Al, which shows improvements of approximately 118% and 26% as compared to those of Al2O3np/Al and SiCnp/Al, respectively. X-ray Diffractometer (XRD) results confirm the presence of SiCnp and Al2O3np in hybrid nanocomposites. The dispersed SiCnp and Al2O3np are homogeneously distributed in the matrix and no clusters consisting of SiCnp and Al2O3np exist in the microstructure. Theoretical analyses also verify that there is little possibility for clusters to form in the melt. Good bonding between nanoparticles and Al is demonstrated. Neither cavities nor reaction products exist at the interface. The ductility and the strength of Al2O3np/SiCnp/Al are improved. The improvement in yield strength of Al2O3np/SiCnp/Al, in comparison with that of A356, is about 45%

Evolutionary Game of Digital-Driven Photovoltaic–Storage–Use Value Chain Collaboration: A Value Intelligence Creation Perspective

In the context of “carbon neutral”, distributed energy, including photovoltaic power generation and energy storage systems, is developing rapidly. Meanwhile, the new generation of information technology, such as “Cloud computing, Big data, the Internet of things, Mobile Internet, AI, Blockchain”, is driving the digital transformation of the energy industry. Under digital drive, how the agents in the photovoltaic–storage–use value chain collaborate and create value intelligently is a question worthy of deep consideration. Firstly, the value creation mechanism and collaborative process of the digital-driven photovoltaic–storage–use value chain are analyzed from a value intelligence creation perspective. Secondly, the tripartite evolutionary game model of photovoltaic power generator, energy storage provider and user is established. Finally, the influencing factors of digital- driven photovoltaic–storage–use value chain collaboration are explored through a numerical simulation, and management suggestions are put forward. The study finds the following: (1) The behavior choice of each agent in the value chain will affect the decision of other agents. In particular, the photovoltaic power generator has a great influence on the cooperative willingness of other agents. To promote value chain collaboration, the guiding role of the photovoltaic power generator should be fully realized. (2) Agents on the value chain can use a variety of digital technologies to improve enabling benefits, which is conducive to promoting value chain collaboration. (3) The driving costs and potential risks are obstacles for value chain collaboration. Cost reduction and risk prevention are effective ways to improve the willingness of collaboration. (4) Reasonable incentive compensation mechanisms and information asymmetry punishment measures are the keys to enhancing collective willingness. This research provides theoretical support for photovoltaic–storage–use value chain collaboration from a value intelligence creation perspective

A digital strategy for intraoperative acquisition of actual drill position and rapid assessment of bony preparation accuracy using an intraoral scanner

A digital workflow to acquire actual position of the drill and assess bony preparation accuracy intraoperatively was described. Based on the widely used intraoral scanner, this digital workflow was a relatively practical and economical option for digital intraoperative measurement. As a result, it could help the clinician in accurate verification and immediate correction of the drill position and consequently facilitating the accurate implant placement in implant surgery

Dental Reimplantation Treatment and Clinical Care for Patients with Previous Implant Failure—A Retrospective Study

(1) Objectives: This study evaluated the clinical outcomes of dental implants placed in previously failed sites and discussed the risk factors that mattered in reimplantation. (2) Methods: All the cases by one specific implantologist during his first five years of clinical practice were screened, with a focus on those who received reimplantation. The clinical outcomes were assessed, including the implant survival, peri-implant health, and patients’ satisfaction. (3) Results: 28 patients (31 implants) were recorded as failures from 847 patients (1269 implants), with a 2.4% overall failure rate at the implant level, of whom 19 patients (21 implants) received reimplantation treatment. After a mean follow-up of 33.7 ± 10.1 months (95% CI 29.1–38.3 months), 20 implants remained functional, but 1 implant revealed a secondary early failure, indicating a 95.2% overall survival rate. The mean probing depth (PD), modified sulcus bleeding index (mSBI), and marginal bone loss (MBL) of the surviving reinserted implants were 2.7 ± 0.6 mm (95% CI 2.5–3.0 mm), 0.7 ± 0.5 (95% CI 0.5–1.0), and 0.5 ± 0.6 mm (95% CI 0.3–0.8 mm), respectively. Embedded healing occurred more frequently in the reinserted implants than in the primary implants (p = 0.052). The patients’ satisfaction suffered from implant failure, but a successful reimplantation could reverse it with close doctor–patient communication. (4) Conclusions: Reimplantation treatment was recommended, based on a thorough evaluation and consideration of the risk factors combined with effective communication with the patients

RDCPF: A Redundancy-Based Duty-Cycling Pipelined-Forwarding MAC for Linear Sensor Networks

Existing duty-cycling and pipelined-forwarding (DCPF) protocols applied in battery-powered wireless sensor networks can significantly alleviate the sleep latency issue and save the energy of networks. However, when a DCPF protocol applies to a linear sensor network (LSN), it lacks the ability to handle the bottleneck issue called the energy-hole problem, which is mainly manifested due to the excessive energy consumption of nodes near the sink node. Without overcoming this issue, the lifespan of the network could be greatly reduced. To that end, this paper proposes a method of deploying redundant nodes in LSN, and a corresponding enhanced DCPF protocol called redundancy-based DCPF (RDCPF) to support the new topology of LSN. In RDCPF, the distribution of energy consumption of the whole network becomes much more even. RDCPF also brings improvements to the network in terms of network survival time, packet delivery latency, and energy efficiency, which have been shown through the extensive simulations in comparison with existing DCPF protocols