CVPR 2021 点云方向相关文章
点云(Point Cloud)
DeepI2P: Image-to-Point Cloud Registration via Deep Classification(通过深度分类的图像到点云配准)
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FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds(FESTA：场景点云通过时空注意进行光流估计)
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Denoise and Contrast for Category Agnostic Shape Completion(类别不可知形状完成的消噪和对比度)
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Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation(无提案的LiDAR点云全景分割)
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ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning(ReAgent：使用模仿和强化学习进行点云配准)
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Equivariant Point Network for 3D Point Cloud Analysis(等变点网络进行3D点云分析)
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PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds(PAConv：点云上具有动态内核组装的位置自适应卷积)
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Skeleton Merger: an Unsupervised Aligned Keypoint Detector(骨架合并：无监督的对准关键点检测器)
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Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding(使用缺失区域编码的循环变换完成不成对的点云)
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Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion(通过双边扩充和自适应融合对实点云场景进行语义分割)
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How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines(线云如何保护隐私？ 从3D线中恢复场景详细信息)
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PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency(使用深度空间一致性进行稳健的点云配准)
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Robust Point Cloud Registration Framework Based on Deep Graph Matching(基于深度图匹配的鲁棒点云配准框架)
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TPCN: Temporal Point Cloud Networks for Motion Forecasting(面向运动预测的时态点云网络) paper | code
PointGuard: Provably Robust 3D Point Cloud Classification(可证明稳健的三维点云分类)
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Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市规模3D点云的语义分割：数据集，基准和挑战)
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SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration(SpinNet：学习用于3D点云配准的通用表面描述符)
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MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通过3D扫描同步进行多主体分割和运动估计)
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Diffusion Probabilistic Models for 3D Point Cloud Generation(三维点云生成的扩散概率模型)
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Style-based Point Generator with Adversarial Rendering for Point Cloud Completion(用于点云补全的对抗性渲染基于样式的点生成器)
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PREDATOR: Registration of 3D Point Clouds with Low Overlap(预测器：低重叠的3D点云的配准)
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三维重建(3D Reconstruction)
Global Transport for Fluid Reconstruction with Learned Self-Supervision(具有自学指导的流体重建的全球运输)
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Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction(全面了解通用对象：建模，分段和重构)
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Reconstructing 3D Human Pose by Watching Humans in the Mirror(通过照镜子中的人来重建3D人的姿势)
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Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors(通过学习局部和全局形状先验的层次结构，促进单视图3D重构中的泛化)
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NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video(单目视频的实时相干3D重建)
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Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction(从时空描述符中学习并行密集对应，以进行有效且鲁棒的4D重建)
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POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture(用于单视图人体体积捕获的姿势引导选择性融合)
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Deep Implicit Moving Least-Squares Functions for 3D Reconstruction(用于3D重构的深层隐式移动最小二乘函数)
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Model-based 3D Hand Reconstruction via Self-Supervised Learning(通过自我监督学习进行基于模型的3D手重建)
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3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction(单视图3D漫画面部重建的数据集和基线方法)
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Learning Compositional Representation for 4D Captures with Neural ODE(使用神经ODE学习4D捕捉的合成表示)
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SMPLicit: Topology-aware Generative Model for Clothed People(穿衣服的人的拓扑感知生成模型)
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PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers（具有透视作物层的3D姿势的几何感知神经重建）
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