GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap scenarios. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer, or GeoTransformer for short, to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it invariant to rigid transformation and robust in low-overlap cases. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to $100$ times acceleration. Extensive experiments on rich benchmarks encompassing indoor, outdoor, synthetic, multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our method improves the inlier ratio by $18{\sim}31$ percentage points and the registration recall by over $7$ points on the challenging 3DLoMatch benchmark. Our code and models are available at \url{https://github.com/qinzheng93/GeoTransformer}.Comment: Accepted by TPAMI. Extended version of our CVPR 2022 paper [arXiv:2202.06688

FRACTURE SIMULATION ANALYSIS OF CONNECTING ROD ON DIESEL ENGINE

The connecting rod of high explosion pressure diesel engine was easily fatigue and fracture; the working dynamic load of connecting rod was obtained based on the multibody dynamics. And on this basis,the fatigue life danger zone was founded based on the dynamic stress recovery method,and fracture simulation was analyzed for the region. The results show that the operating cycles reduced from millions to ten thousand times after fatigue cracks taking place,so once the crack was founded,it was the best time to replace the connecting rod to avoid catastrophic accidents

Application of AVO Information-constrained Matching Pursuit Technique in Rich Coal Reservoir Characterization

To address the low accuracy of reservoir characterization in XiHu Sag in a coal-rich environment, this study developed a matching pursuit technology based on AVO information constraints combined with the AVO intercept and gradient characteristics of coal. It can suppress the strong reflectance lithologic artifacts caused by coal and highlight actual and effective reservoir signals. Based on the negative intercept P and positive gradient G of the AVO of coal, the seismic–sensitive factor P–G of coal identification was developed to amplify the seismic response of coal and suppress the high-amplitude response of non-coal. Then, to accurately identify the location of coal, the seismic information of coal was used as the original signal that needs to be decomposed and reconstructed by matching pursuit. Additionally, the efficiency of signal-matching decomposition was improved using the technology of complex seismic track analysis. Finally, the strong reflection elimination of coal was completed. Model trials and practical applications indicate that this method could accurately identify the seismic response location of coal and improve the efficiency of the matching pursuit algorithm. Moreover, the coal-eliminated seismic data can better highlight the lateral distribution changes of the reservoir and improve the vertical characterization accuracy of the main gas layer

Application of Double Width Seismic Data to Channel Sand Body Prediction of X Gas Field in the East China Sea

Due to that the main reservoirs of the X gas field in the East China Sea were deeply buried and have large lateral changes, the conventional seismic data resulted in poor quality and low resolution, which couldn’t meet the increasingly refined geological requirements in exploration and development. Seismic data with wideband and wide azimuth was obtained by using the acquisition method of three ships and four sources with oblique cables, which held the characteristics of high resolution, high signal-to-noise ratio and high fidelity. By taking advantage of the superior information of wideband and wide azimuth seismic data which was high-resolutional and anisotropic, combined with the simultaneous prestack inversion, the inversion body of sensitive elastic parameters of channel sand bodies in different azimuths can be obtained, and superimpose multiple azimuth inversion bodies perpendicular to the direction of the river channel to carry out fine predictions of channel sand bodies. Compared the conventional seismic data, reservoir inversion based on wideband and wide azimuth seismic data improved the prediction accuracy of channel sand bodies, laying a foundation for the progressive exploration and development of the X gas field in the East China Sea

Uniform pooling for graph networks

Abstract The graph convolution network has received a lot of attention because it extends the convolution to non-Euclidean domains. However, the graph pooling method is still less concerned, which can learn coarse graph embedding to facilitate graph classification. Previous pooling methods were based on assigning a score to each node and then pooling only the highest-scoring nodes, which might throw away whole neighbourhoods of nodes and therefore information. Here, we proposed a novel pooling method UGPool with a new point-of-view on selecting nodes. UGPool learns node scores based on node features and uniformly pools neighboring nodes instead of top nodes in the score-space, resulting in a uniformly coarsened graph. In multiple graph classification tasks, including the protein graphs, the biological graphs and the brain connectivity graphs, we demonstrated that UGPool outperforms other graph pooling methods while maintaining high efficiency. Moreover, we also show that UGPool can be integrated with multiple graph convolution networks to effectively improve performance compared to no pooling

Lifespan Development of the Human Brain Revealed by Large-Scale Network Eigen-Entropy

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying functional connectivity patterns of the developing and aging brain. Normal brain development is characterized by continuous and significant network evolution through infancy, childhood, and adolescence, following specific maturational patterns. Normal aging is related to some resting state brain networks disruption, which are associated with certain cognitive decline. It is a big challenge to design an integral metric to track connectome evolution patterns across the lifespan, which is to understand the principles of network organization in the human brain. In this study, we first defined a brain network eigen-entropy (NEE) based on the energy probability (EP) of each brain node. Next, we used the NEE to characterize the lifespan orderness trajectory of the whole-brain functional connectivity of 173 healthy individuals ranging in age from 7 to 85 years. The results revealed that during the lifespan, the whole-brain NEE exhibited a significant non-linear decrease and that the EP distribution shifted from concentration to wide dispersion, implying orderness enhancement of functional connectome over age. Furthermore, brain regions with significant EP changes from the flourishing (7–20 years) to the youth period (23–38 years) were mainly located in the right prefrontal cortex and basal ganglia, and were involved in emotion regulation and executive function in coordination with the action of the sensory system, implying that self-awareness and voluntary control performance significantly changed during neurodevelopment. However, the changes from the youth period to middle age (40–59 years) were located in the mesial temporal lobe and caudate, which are associated with long-term memory, implying that the memory of the human brain begins to decline with age during this period. Overall, the findings suggested that the human connectome shifted from a relatively anatomical driven state to an orderly organized state with lower entropy

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD