Moreover, an automatic repair strategy is proposed to repair syntax/semantic errors in invalid test cases. Although promising, a paucity of empirical research addressed the cost/benefit of incorporating many EBD concepts into one hospital setting, and there was no research that articulated the organizational decision-making process used by healthcare administrators when considering the use of EBD in expansion projects. Unlike past spacecraft power systems, the SSF EPS will grow and be maintained on orbit and must be flexible to meet challenging user power needs. Towards a formal definition of static and dynamic electronic correlations. Taking into account the first non-Markovian correction to the Balescu-Lenard equation, we have derived an expression for the pair correlation function and a nonlinear kinetic equation valid for a nonideal polarized classical plasma. The first 100 UK-based dental practice websites were pooled and saved following duplicate removal. Anticipating Visual Representations from Unlabeled Video. However, coverage tracing provided by existing software-based approaches, such as source instrumentation and dynamic binary translation, can incur large overhead. Are Mutants a Valid Substitute for Real Faults in Software Testing? Fully 3D-Printed Rubik's Cube Solving Robot. We implemented a prototype tool, FaFuzzer, and evaluated it on two different datasets consisting of a variety of real-world applications. In this work, we present the design and implementation of PTrix, which fully unleashes the benefits of PT for fuzzing via three novel techniques. A versatile simulator for robotic grasping, [paper] [code], [arXiv] Pick-Place With Uncertain Object Instance Segmentation and Shape Completion, [paper], [arXiv] Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity, [paper], [ICRA] Learning Continuous 3D Reconstructions for Geometrically Aware Grasping, [paper] [code], [arXiv] Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction, [paper], [arXiv] ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation, [paper], [arXiv] kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion, [paper] [code], [arXiv] Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks, [paper], [IROS] Robust Grasp Planning Over Uncertain Shape Completions, [paper], [arXiv] Multi-Modal Geometric Learning for Grasping and Manipulation, [paper], [ICRA] Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations, [paper], [IROS] 3D Shape Perception from Monocular Vision, Touch, and Shape Priors, [paper], [IROS] Shape Completion Enabled Robotic Grasping, [paper], [arXiv] ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion, [paper], [CVPR] Variational Relational Point Completion Network, [paper], [CVPR] View-Guided Point Cloud Completion, [paper], [arXiv] 3D Semantic Scene Completion: a Survey, [paper], [CVPR] Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding, [paper], [CVPR] Style-based Point Generator with Adversarial Rendering for Point Cloud Completion, [paper], [CVPR] Diffusion Probabilistic Models for 3D Point Cloud Generation, [paper], [arXiv] DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates, [paper], [arXiv] Generation for adaption: a Gan-based approach for 3D Domain Adaption in Point Cloud, [paper], [arXiv] HyperPocket: Generative Point Cloud Completion, [paper], [arXiv] Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences, [paper], [arXiv] PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths, [paper], [arXiv] Towards Part-Based Understanding of RGB-D Scans, [paper], [arXiv] Learning geometry-image representation for 3D point cloud generation, [paper], [arXiv] Diverse Plausible Shape Completions from Ambiguous Depth Images, [paper], [arXiv] A Self-supervised Cascaded Refinement Network for Point Cloud Completion, [paper], [arXiv] Refinement of Predicted Missing Parts Enhance Point Cloud Completion, [paper], [3DV] A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds, [paper], [NeurIPS] Skeleton-bridged Point Completion: From Global Inference to Local Adjustment, [paper], [arXiv] Pre-Training by Completing Point Clouds, [paper], [ECCVW] Implicit Feature Networks for Texture Completion from Partial 3D Data, [paper], [arXiv] LMSCNet: Lightweight Multiscale 3D Semantic Completion, [paper], [arXiv] Self-Sampling for Neural Point Cloud Consolidation, [paper], [ECCV] PointMixup: Augmentation for Point Clouds, [paper], [ECCV] Learning Gradient Fields for Shape Generation, [paper], [ECCV] SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification, [paper], [ECCV] Weakly-supervised 3D Shape Completion in the Wild, [paper], [arXiv] VPC-Net: Completion of 3D Vehicles from MLS Point Clouds, [paper], [arXiv] LPMNet: Latent Part Modification and Generation for 3D Point Clouds, [paper], [arXiv] DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry, [paper], [arXiv] KAPLAN: A 3D Point Descriptor for Shape Completion, [paper], [arXiv] Point Cloud Completion by Learning Shape Priors, [paper], [TOG] SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images, [paper], [arXiv] MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement, [paper], [arXiv] Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows, [paper] [project], [ECCV] Discrete Point Flow Networks for Efficient Point Cloud Generation, [paper], [arXiv] Progressive Point Cloud Deconvolution Generation Network, [paper], [arXiv] Point Set Voting for Partial Point Cloud Analysis, [paper], [arXiv] 3D Topology Transformation with Generative Adversarial Networks, [paper], [arXiv] Detail Preserved Point Cloud Completion via Separated Feature Aggregation, [paper], [arXiv] Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion, [paper], [arXiv] GRNet: Gridding Residual Network for Dense Point Cloud Completion, [paper], [RAL] GFPNet: A Deep Network for Learning Shape Completion in Generic Fitted Primitives, [paper], [arXiv] Point Cloud Completion by Skip-attention Network with Hierarchical Folding, [paper], [arXiv] PointTriNet: Learned Triangulation of 3D Point Sets, [paper], [arXiv] DeepSDF x Sim(3): Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation, [paper], [arXiv] Anisotropic Convolutional Networks for 3D Semantic Scene Completion, [paper], [arXiv] Cascaded Refinement Network for Point Cloud Completio, [paper], [arXiv] Generative PointNet: Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification, [paper], [arXiv] Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation, [paper], [arXiv] Modeling 3D Shapes by Reinforcement Learning, [paper], [arXiv] PF-Net: Point Fractal Network for 3D Point Cloud Completion, [paper], [arXiv] Hypernetwork approach to generating point clouds, [paper], [arXiv] Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion, [paper], [arXiv] PolyGen: An Autoregressive Generative Model of 3D Meshes, [paper], [arXiv] BlockGAN Learning 3D Object-aware Scene Representations from Unlabelled Images, [paper], [arXiv] Implicit Geometric Regularization for Learning Shapes, [paper], [arXiv] The Whole Is Greater Than the Sum of Its Nonrigid Parts, [paper], [arXiv] PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions, [paper], [arXiv] Multimodal Shape Completion via Conditional Generative Adversarial Networks, [paper], [arXiv] Symmetry Detection of Occluded Point Cloud Using Deep Learning, [paper], [arXiv] Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter, [paper], [3DORW] Completion of Cultural Heritage Objects with Rotational Symmetry, [paper], [arXiv] Single Image Depth Estimation: An Overview, [paper], [CVPR] Depth Completion using Plane-Residual Representation, [paper], [arXiv] LEAD: LiDAR Extender for Autonomous Driving, [paper], [arXiv] Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications, [paper], [IROS] Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera, [paper], [BMVC] DESC: Domain Adaptation for Depth Estimation via Semantic Consistency, [paper] [code], [arXiv] Adaptive Context-Aware Multi-Modal Network for Depth Completion, [paper], [arXiv] Depth Completion with RGB Prior, [paper], [IROS] Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP, [paper], [arXiv] Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets, [paper], [ECCV] Feature-metric Loss for Self-supervised Learning of Depth and Egomotion, [paper], [ECCV] Non-Local Spatial Propagation Network for Depth Completion, [paper] [code], [IROS] UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models, [paper], [IROS] 360 Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron, [paper], [ECCV] Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance, [paper], [ECCV] P2Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation, [paper], [arXiv] P2D: a self-supervised method for depth estimation from polarimetry, [paper], [arXiv] MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation, [paper], [RAL] Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor, [paper], [arXiv] Increased-Range Unsupervised Monocular Depth Estimation, [paper], [arXiv] Targeted Adversarial Perturbations for Monocular Depth Prediction, [paper], [arXiv] AcED: Accurate and Edge-consistent Monocular Depth Estimation, [paper], [arXiv] Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues, [paper], [arXiv] Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping, [paper], [arXiv] Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End, [paper], [arXiv] A Survey on Deep Learning Techniques for Stereo-based Depth Estimation, [paper], [arXiv] Real-time single image depth perception in the wild with handheld devices, [paper], [arXiv] SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation, [paper], [arXiv] PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation, [paper], [CVPR] Bi3D: Stereo Depth Estimation via Binary Classifications, [paper], [CVPR] Focus on defocus: bridging the synthetic to real domain gap for depth estimation, [paper], [arXiv] Decoder Modulation for Indoor Depth Completion, [paper], [CVPR] On the uncertainty of self-supervised monocular depth estimation, [paper] [code], [arXiv] Consistent Video Depth Estimation, [paper], [arXiv] Self-Supervised Attention Learning for Depth and Ego-motion Estimation, [paper], [arXiv] Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction, [paper], [arXiv] DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation, [paper], [arXiv] RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes, [paper], [arXiv] Monocular Depth Estimation with Self-supervised Instance Adaptation, [paper], [arXiv] Guiding Monocular Depth Estimation Using Depth-Attention Volume, [paper], [arXiv] 3D Photography using Context-aware Layered Depth Inpainting, [paper], [arXiv] Occlusion-Aware Depth Estimation with Adaptive Normal Constraints, [paper], [arXiv] The Edge of Depth: Explicit Constraints between Segmentation and Depth, [paper], [arXiv] Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume, [paper], [arXiv] DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning, [paper], [arXiv] Adversarial Attacks on Monocular Depth Estimation, [paper], [arXiv] Monocular Depth Prediction Through Continuous 3D Loss, [paper], [arXiv] 3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image, [paper], [arXiv] Depth Estimation by Learning Triangulation and Densification of Sparse Points for Multi-view Stereo, [paper], [arXiv] Monocular Depth Estimation Based On Deep Learning: An Overview, [paper], [arXiv] Scene Completenesss-Aware Lidar Depth Completion for Driving Scenario, [paper], [arXiv] Fast Depth Estimation for View Synthesis, [paper], [arXiv] Active Depth Estimation: Stability Analysis and its Applications, [paper], [arXiv] Uncertainty depth estimation with gated images for 3D reconstruction, [paper], [arXiv] Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry, [paper], [arXiv] A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation, [paper], [arXiv] Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields, [paper], [ICLR] Semantically-Guided Representation Learning for Self-Supervised Monocular Depth, [paper], [arXiv] 3D Gated Recurrent Fusion for Semantic Scene Completion, [paper], [arXiv] Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot, [paper], [arXiv] Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution, [paper], [arXiv] DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data, [paper], [arXiv] Depth Map Estimation of Dynamic Scenes Using Prior Depth Information, [paper], [arXiv] FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation, [paper], [ICRA] Depth Based Semantic Scene Completion with Position Importance Aware Loss, [paper], [arXiv] ResDepth: Learned Residual Stereo Reconstruction, [paper], [arXiv] Single Image Depth Estimation Trained via Depth from Defocus Cues, [paper], [arXiv] RoutedFusion: Learning Real-time Depth Map Fusion, [paper], [arXiv] Don't Forget The Past: Recurrent Depth Estimation from Monocular Video, [paper], [AAAI] Morphing and Sampling Network for Dense Point Cloud Completion, [paper] [code], [AAAI] CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion, [paper], [arXiv] Normal Assisted Stereo Depth Estimation, [paper], [arXiv] Geometry-aware Generation of Adversarial and Cooperative Point Clouds, [paper], [arXiv] DeepSFM: Structure From Motion Via Deep Bundle Adjustment, [paper], [CVIU] On the Benefit of Adversarial Training for Monocular Depth Estimation, [paper], [ICCV] Learning Joint 2D-3D Representations for Depth Completion, [paper], [ICCV] Deep Optics for Monocular Depth Estimation and 3D Object Detection, [paper], [arXiv] Deep Classification Network for Monocular Depth Estimation, [paper], [ICCV] Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints, [paper], [arXiv] Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era, [paper], [arXiv] Real-time Vision-based Depth Reconstruction with NVidia Jetson, [paper], [IROS] Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics, [paper], [arXiv] Monocular depth estimation: a survey, [paper], [3DV] PCN: Point Completion Network, [paper] [code], [NeurIPS] Learning to Reconstruct Shapes from Unseen Classes, [paper] [code], [ECCV] Learning Shape Priors for Single-View 3D Completion and Reconstruction, [paper] [code], [CVPR] Deep Depth Completion of a Single RGB-D Image, [paper] [code], [arXiv] SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization, [paper], [arXiv] Deep Magnification-Arbitrary Upsampling over 3D Point Clouds, [paper], [arXiv] CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling, [paper], [MM] Differentiable Manifold Reconstruction for Point Cloud Denoising, [paper], [arXiv] A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain , [paper], [arXiv] Learning Graph-Convolutional Representations for Point Cloud Denoising, [paper], [arXiv] MOPS-Net: A Matrix Optimization-driven Network for Task-Oriented 3D Point Cloud Downsampling, [paper], [arXiv] Deep Feature-preserving Normal Estimation for Point Cloud Filtering, [paper], [arXiv] Self-Supervised Learning for Domain Adaptation on Point-Clouds, [paper], [arXiv] Non-Local Part-Aware Point Cloud Denoising, [paper], [arXiv] PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling, [paper], [arXiv] CNN-based Lidar Point Cloud De-Noising in Adverse Weather, [paper], [arXiv] PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks, [paper] [code], [ICCV] PU-GAN: a Point Cloud Upsampling Adversarial Network, [paper] [code], [CVPR] Patch-based Progressive 3D Point Set Upsampling, [paper] [code], [arXiv] SampleNet: Differentiable Point Cloud Sampling, [paper] [code], [CVPR] PU-Net: Point Cloud Upsampling Network, [paper] [code], [IROS] Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks, [paper], [RSSW] Self-Supervised Goal-Conditioned Pick and Place, [paper], [arXiv] Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation, [paper], [arXiv] Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation, [paper], [TOR] Learning Transferable Push Manipulation Skills in Novel Contexts, [paper], [RAL] Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects, [paper], [arXiv] Vision-based control of a knuckle boom crane with online cable length estimation, [paper], [arXiv] A Point Cloud-Based Method for Automatic Groove Detection and Trajectory Generation of Robotic Arc Welding Tasks, [paper], [arXiv] Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation, [paper], [arXiv] Combinatorial 3D Shape Generation via Sequential Assembly, [paper], [arXiv] Learning visual policies for building 3D shape categories, [paper], [arXiv] Where to relocate?

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