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

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

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits

Rabbits are an attractive meat livestock species that can efficiently convert human-indigestible plant biomass, and have been commonly used in biological and medical researches. Yet, transcriptomic landscape in muscle tissue and association between gene expression level and growth traits have not been specially studied in meat rabbits. In this study Oxford Nanopore Technologies (ONT) long-read sequencing technology was used for comprehensively exploring transcriptomic landscape in Longissimus dorsi for 115 rabbits at 84 days of age, and transcriptome-wide association studies (TWAS) were performed for growth traits, including body weight at 84 days of age and average daily gain during three growth periods. The statistical analysis of TWAS was performed using a mixed linear model, in which polygenic effect was fitted as a random effect according to gene expression level-based relationships. A total of 18,842 genes and 42,010 transcripts were detected, among which 35% of genes and 47% of transcripts were novel in comparison with the reference genome annotation. Furthermore, 45% of genes were widely expressed among more than 90% of individuals. The proportions (±SE) of phenotype variance explained by genome-wide gene expression level ranged from 0.501 ± 0.216 to 0.956 ± 0.209, and the similar results were obtained when explained by transcript expression level. In contrast, neither gene nor transcript was detected by TWAS to be statistically significantly associated with these growth traits. In conclusion, these novel genes and transcripts that have been extensively profiled in a single muscle tissue using long-read sequencing technology will greatly improve our understanding on transcriptional diversity in rabbits. Our results with a relatively small sample size further revealed the important contribution of global gene expression to phenotypic variation on growth performance, but it seemed that no single gene has an outstanding effect; this knowledge is helpful to include intermediate omics data for implementing genetic evaluation of growth traits in meat rabbits

A genome-wide association study of coat color in Chinese Rex rabbits

Coat color is an important phenotypic characteristic of the domestic rabbit (Oryctolagus cuniculus) and has specific economic importance in the Rex rabbit industry. Coat color varies considerably among different populations of rabbits, and several causal genes for this variation have been thoroughly studied. Nevertheless, the candidate genes affecting coat color variation in Chinese Rex rabbits remained to be investigated. In this study, we collected blood samples from 250 Chinese Rex rabbits with six different coat colors. We performed genome sequencing using a restriction site-associated DNA sequencing approach. A total of 91,546 single nucleotide polymorphisms (SNPs), evenly distributed among 21 autosomes, were identified. Genome-wide association studies (GWAS) were performed using a mixed linear model, in which the individual polygenic effect was fitted as a random effect. We detected a total of 24 significant SNPs that were located within a genomic region on chromosome 4 (OCU4). After re-fitting the most significant SNP (OCU4:13,434,448, p = 1.31e-12) as a covariate, another near-significant SNP (OCU4:11,344,946, p = 7.03e-07) was still present. Hence, we conclude that the 2.1-Mb genomic region located between these two significant SNPs is significantly associated with coat color in Chinese Rex rabbits. The well-studied coat-color-associated agouti signaling protein (ASIP) gene is located within this region. Furthermore, low genetic differentiation was also observed among the six coat color varieties. In conclusion, our results confirmed that ASIP is a putative causal gene affecting coat color variation in Chinese Rex rabbits

Experiment research of the influence of different cooling times on the drillability of high-temperature granite

To study the drillability and microscopic damage changes of granite after high temperature exposure and to study the influence of different cooling times on drillability after high temperature exposure, granite for high-temperature heat treatment was cooled by different times (2 h, 4 h, 24 h, and 48 h). Through the drillability experiment and the cast thin identification of the processed rock samples, the influence law and mechanism of high temperature on the drillability of granite were obtained, and the influence of different cooling times on the granite drillability was also investigated. The research results show that granite always maintains a high drillability index under the constraints of heat treatment not exceeding 500℃ and natural cooling for 2 h. After cooling 4 h, 24 h, and 48 h, the impact of high temperature on drillability shows three stages (First degradation stage, strengthening stage, second degradation stage). The location and number of microcracks affect the difficulty of rock resistance to crushing. After 400℃ heat treatment, the internal microcracks of the granite begin to increase significantly. When the microcracks generate a large number of microcracks inside the quartz particles, the drillability of the granite is significantly reduced. After heat treatment at 100℃, cooling for no more than 4 h at the same time will significantly affect the drillability of granite. After heat treatment at 200-400℃, the drillability index of granite will increase significantly as the cooling time (4-48 h) continues to increase. The damage caused by 500℃ to granite is irreversible, and 600℃ has completely degraded the granite. Understanding the influence of high temperature and cooling time on the drillability of granite can provide basic theoretical support for the efficient exploitation of hot dry rock resources

Effects of Cyclic Heating and Water Cooling on the Physical Characteristics of Granite

This paper aims to study the effect of cyclic heating and flowing-water cooling conditions on the physical properties of granite. Ultrasonic tests, gas measured porosity, permeability, and microscope observations were conducted on granite after thermal treatment. The results showed that the velocity of P- and S-waves decreased as the number of thermal cycles increased. The porosity increased with the number of the thermal cycles attained at 600 °C, while no apparent changes were observed at 200 and 400 °C. The permeability increased with the increasing number of thermal cycles. Furthermore, microscope observations showed that degradation of the granite after thermal treatment was attributed to a large network of microcracks induced by thermal stress. As the number of thermal cycles increased, the number of transgranular microcracks gradually increased, as well as their length and width. The quantification of microcracks from cast thin section (CTS) images supported the visual observation

Logging identification of high-quality shale of the marine-continent transitional facies: An example of the Shan 2 Member of the Daning-Jixian area in the Ordos Basin

On the southeastern margin of the Ordos Basin, the mineral composition of marine-continent transitional facies deposits is complex. The shale, sandstone, coal, and related lithofacies frequently interact, and the lithology changes rapidly in the vertical direction. Due to the low resolution of conventional logging method and borehole enlargement which is a common while drilling, the commonly used methods for identification of lithology including high-quality shale which is prevailing in marine shale gas evaluation are less effective for the study area. First, deconvolution technology was used to improve the resolution of natural gamma rays, gamma rays without uranium and uranium logging curves. Then, a log cross-plot was used to identify the lithology including shale of marine-continent transitional facies, and the uranium-spontaneous potential curve overlap method was proposed to identify high-quality shale from marine-continent transitional facies. The results show that the deconvolution method can effectively improve the vertical resolution of natural gamma rays, gamma rays without uranium and uranium logging curves. The cross-plot of natural gamma-density logging data has a better effect on identifying the lithology of the marine-continent transitional facies, and the cross-plot of uranium logging data and gamma ray data without uranium can further identify three types of shale lithofacies (calcareous siliceous shale, siliceous clay shale and clay shale). In the marine-continent transitional facies, the newly proposed uranium-spontanous potential overlap method is better than the traditional ΔlogR method inidentifying high-quality shale. This research can provide theoretical support for reservoir evaluation of marine-continent transitional shale gas and improve the accuracy of high-quality shale identification