CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm

Point cloud completion aims to predict complete shape from its partial observation. Current approaches mainly consist of generation and refinement stages in a coarse-to-fine style. However, the generation stage often lacks robustness to tackle different incomplete variations, while the refinement stage blindly recovers point clouds without the semantic awareness. To tackle these challenges, we unify point cloud Completion by a generic Pretrain-Prompt-Predict paradigm, namely CP3. Inspired by prompting approaches from NLP, we creatively reinterpret point cloud generation and refinement as the prompting and predicting stages, respectively. Then, we introduce a concise self-supervised pretraining stage before prompting. It can effectively increase robustness of point cloud generation, by an Incompletion-Of-Incompletion (IOI) pretext task. Moreover, we develop a novel Semantic Conditional Refinement (SCR) network at the predicting stage. It can discriminatively modulate multi-scale refinement with the guidance of semantics. Finally, extensive experiments demonstrate that our CP3 outperforms the state-of-the-art methods with a large margin

Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud

This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, points with similar local textures but different categories, and points in isolate small hard areas, which largely harm the performance of 3D semantic segmentation. To address this challenge, we propose a novel Indistinguishable Area Focalization Network (IAF-Net), which selects indistinguishable points adaptively by utilizing the hierarchical semantic features and enhances fine-grained features for points especially those indistinguishable points. We also introduce multi-stage loss to improve the feature representation in a progressive way. Moreover, in order to analyze the segmentation performances of indistinguishable areas, we propose a new evaluation metric called Indistinguishable Points Based Metric (IPBM). Our IAF-Net achieves the comparable results with state-of-the-art performance on several popular 3D point cloud datasets e.g. S3DIS and ScanNet, and clearly outperforms other methods on IPBM.Comment: AAAI202

MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency

Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains an open problem due to the data sparsity and scene complexity. The conventional random masking paradigm used in 2D images often causes a high risk of ambiguity when recovering the masked region of 3D scenes. To this end, we propose a novel informative-preserved reconstruction, which explores local statistics to discover and preserve the representative structured points, effectively enhancing the pretext masking task for 3D scene understanding. Integrated with a progressive reconstruction manner, our method can concentrate on modeling regional geometry and enjoy less ambiguity for masked reconstruction. Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas. By elegantly combining informative-preserved reconstruction on masked areas and consistency self-distillation from unmasked areas, a unified framework called MM-3DScene is yielded. We conduct comprehensive experiments on a host of downstream tasks. The consistent improvement (e.g., +6.1 [email protected] on object detection and +2.2% mIoU on semantic segmentation) demonstrates the superiority of our approach

Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud

In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat area of 3D objects, such as the boundary and seat area of a chair, describe different but also complementary geometries. However, such investigation is lost in previous deep networks that understand point clouds by directly treating all points or local patches equally. To solve this problem, we propose Geometry-Disentangled Attention Network (GDANet). GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components. Then GDANet exploits Sharp-Gentle Complementary Attention Module that regards the features from sharp and gentle variation components as two holistic representations, and pays different attentions to them while fusing them respectively with original point cloud features. In this way, our method captures and refines the holistic and complementary 3D geometric semantics from two distinct disentangled components to supplement the local information. Extensive experiments on 3D object classification and segmentation benchmarks demonstrate that GDANet achieves the state-of-the-arts with fewer parameters. Code is released on https://github.com/mutianxu/GDANet.Comment: Accepted by AAAI202

Advances and Promises of 2D MXenes as Cocatalysts for Artificial Photosynthesis

2D MXenes have received growing attention as highly active and earth-abundant cocatalysts for photocatalytic applications, due to their merits including rich surface chemistry, excellent electrical conductivity, distinct carrier mobility, and good hydrophilicity. In this review, the research progress on MXenes and MXene-based composites in photocatalysis are systematically summarized and discussed. Based on the theoretical and experimental studies, this review focuses mainly on the synthetic methods of MXenes, the roles of MXenes in photocatalysis, the design strategies for MXene-based composites and the state-of-the-art applications of these MXene-based materials in photocatalytic reactions, including water splitting, CO2 reduction, and nitrogen fixation. In addition, this review outlines the surface modification and enhancement mechanism from a material design and synthesis perspective, and highlights the pivotal challenges and potential opportunities in the field of MXene-involved photocatalysis. Therefore, this review is expected to provide a useful reference for the development of MXene-related photocatalytic applications.</p

A new method to estimate slab dip direction using receiver functions and its application in revealing slab geometry and a diffuse plate boundary beneath Sumatra

While dip direction is a fundamental parameter of slab geometry, it is rarely estimated quantitatively. Here, we develop a new method, Dip Direction Searching (DDS), of receiver functions (RFs) that reduces the uncertainty of slab dip direction estimation from tens to several degrees. DDS can also resolve the thickness and depth of a dipping structure. We then apply DDS to the RFs in the Sumatran subduction zone. Travel time differences of the converted phases from the upper and lower (oceanic Moho) boundaries of the dipping low-velocity layer (LVL) along the plate interface show a thickness of 10–14 km. The results also show increased dip direction of the slab Moho from 47 ± 5.3° in southern Sumatra to 70 ± 10.7° in northern Sumatra, indicating a complicated slab geometry and internal deformation along strike. Similar dip directions are obtained for the upper and lower LVL boundaries beneath Nias and Enggano forearc islands in the north and south, whereas we find a larger discrepancy of ∼14–23° beneath Siberut and Pagai in between. The thicker LVL with a non-negligible difference in the dip directions of its upper and lower bounds in the center of Sumatra is interpreted as a partially serpentinized mantle layer above the oceanic crust, forming a distinct channel atop the subducting slab. Our results provide basic observational constraints on the structure and geometry of the oceanic slab and associated subduction processes. Both synthetics and data analyses also indicate DDS can be applied in other subduction zones and for other dipping interfaces.Ministry of Education (MOE)National Research Foundation (NRF)Published versionThis research is jointly supported by the National Natural Science Foundation of China (Grant 42288201), the Strategic Priority Research Program (A) of Chinese Academy of Sciences (Grant XDA20070302) and Singapore MOE tier-2 Grant (MOE2019-T2-1-182 (S)). This research was supported by the Earth Observatory of Singapore via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative. This work comprises EOS contribution number 522. M. Feng thanks the Chinese Scholarship Council for scholarship fund

On-Board Liquid Hydrogen Cold Energy Utilization System for a Heavy-Duty Fuel Cell Hybrid Truck

In this paper, a kind of on-board liquid hydrogen (LH2) cold energy utilization system for a heavy-duty fuel cell hybrid truck is proposed. Through this system, the cold energy of LH2 is used for cooling the inlet air of a compressor and the coolant of the accessories cooling system, sequentially, to reduce the parasitic power, including the air compressor, water pump, and radiator fan power. To estimate the cold energy utilization ratio and parasitic power saving capabilities of this system, a model based on AMESim software was established and simulated under different ambient temperatures and fuel cell stack loads. The simulation results show that cold energy utilization ratio can keep at a high level except under extremely low ambient temperature and light load. Compared to the original LH2 system without cold energy utilization, the total parasitic power consumption can be saved by up to 15% (namely 1.8 kW)