{"total":101,"rows":[{"id":"2111.06881","title":"Multimodal Virtual Point 3D Detection","created":"2021-11-12","cited_by_count":166,"area":null},{"id":"2105.14021","title":"Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging","created":"2021-05-28","cited_by_count":104,"area":null},{"id":"2103.16397","title":"3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding","created":"2021-03-30","cited_by_count":100,"area":null},{"id":"2108.06709","title":"SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation","created":"2021-08-15","cited_by_count":94,"area":null},{"id":"2107.10981","title":"Score-Based Point Cloud Denoising","created":"2021-07-23","cited_by_count":83,"area":null},{"id":"2103.07466","title":"3D Semantic Scene Completion: a Survey","created":"2021-03-12","cited_by_count":74,"area":null},{"id":"2106.04779","title":"Point Cloud Upsampling via Disentangled Refinement","created":"2021-06-09","cited_by_count":74,"area":null},{"id":"2103.12266","title":"Deep Implicit Moving Least-Squares Functions for 3D Reconstruction","created":"2021-03-23","cited_by_count":67,"area":null},{"id":"2202.09507","title":"PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths","created":"2022-02-19","cited_by_count":57,"area":null},{"id":"2209.13232","title":"A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective","created":"2022-09-27","cited_by_count":56,"area":null},{"id":"2202.13589","title":"Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey","created":"2022-02-28","cited_by_count":54,"area":null},{"id":"2112.04148","title":"Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling","created":"2021-12-08","cited_by_count":48,"area":null},{"id":"2104.05666","title":"View-Guided Point Cloud Completion","created":"2021-04-12","cited_by_count":46,"area":null},{"id":"2304.11846","title":"Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions","created":"2023-04-24","cited_by_count":43,"area":null},{"id":"2204.12684","title":"Density-preserving Deep Point Cloud Compression","created":"2022-04-27","cited_by_count":39,"area":null},{"id":"2202.09367","title":"Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer","created":"2022-02-18","cited_by_count":38,"area":null},{"id":"2308.11441","title":"Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection","created":"2023-08-22","cited_by_count":36,"area":null},{"id":"2111.12242","title":"PU-Transformer: Point Cloud Upsampling Transformer","created":"2021-11-24","cited_by_count":32,"area":null},{"id":"2105.01604","title":"Orienting Point Clouds with Dipole Propagation","created":"2021-05-04","cited_by_count":31,"area":null},{"id":"2211.16762","title":"GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation","created":"2022-11-30","cited_by_count":29,"area":null},{"id":"2312.02719","title":"A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling","created":"2023-12-03","cited_by_count":28,"area":"diffusion"},{"id":"2111.02045","title":"Deep Point Set Resampling via Gradient Fields","created":"2021-11-03","cited_by_count":24,"area":null},{"id":"2204.08196","title":"Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation","created":"2022-04-18","cited_by_count":24,"area":null},{"id":"2308.12113","title":"Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey","created":"2023-08-23","cited_by_count":24,"area":null},{"id":"2401.01552","title":"CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers","created":"2024-01-03","cited_by_count":21,"area":null},{"id":"2503.14558","title":"SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization","created":"2025-03-18","cited_by_count":21,"area":"wm"},{"id":"2212.08892","title":"Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis","created":"2022-12-17","cited_by_count":19,"area":null},{"id":"2312.15133","title":"Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling","created":"2023-12-23","cited_by_count":19,"area":null},{"id":"2203.00914","title":"PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling","created":"2022-03-02","cited_by_count":18,"area":null},{"id":"2107.05893","title":"PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows","created":"2021-07-13","cited_by_count":17,"area":null},{"id":"2108.00454","title":"SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering","created":"2021-08-01","cited_by_count":17,"area":null},{"id":"2204.10750","title":"PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling","created":"2022-04-22","cited_by_count":16,"area":null},{"id":"2111.11704","title":"Deep Point Cloud Reconstruction","created":"2021-11-23","cited_by_count":15,"area":null},{"id":"2210.03942","title":"Point Cloud Upsampling via Cascaded Refinement Network","created":"2022-10-08","cited_by_count":14,"area":null},{"id":"2109.09337","title":"PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling","created":"2021-09-20","cited_by_count":11,"area":null},{"id":"2209.06407","title":"Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection","created":"2022-09-14","cited_by_count":10,"area":null},{"id":"2308.16484","title":"Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning","created":"2023-08-31","cited_by_count":10,"area":null},{"id":"2408.06286","title":"Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering","created":"2024-08-12","cited_by_count":9,"area":null},{"id":"2104.05328","title":"RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut $2^D$-Tree Representation","created":"2021-04-12","cited_by_count":8,"area":null},{"id":"2301.13558","title":"Lidar Upsampling with Sliced Wasserstein Distance","created":"2023-01-31","cited_by_count":8,"area":null},{"id":"2208.05274","title":"Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussians","created":"2022-08-10","cited_by_count":6,"area":null},{"id":"2403.05117","title":"Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning","created":"2024-03-08","cited_by_count":6,"area":null},{"id":"2111.15513","title":"RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising","created":"2021-11-30","cited_by_count":5,"area":null},{"id":"2505.10601","title":"SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds","created":"2025-05-15","cited_by_count":5,"area":"ssm"},{"id":"2202.03901","title":"HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving","created":"2022-02-08","cited_by_count":4,"area":null},{"id":"2203.10768","title":"Upsampling Autoencoder for Self-Supervised Point Cloud Learning","created":"2022-03-21","cited_by_count":4,"area":null},{"id":"2310.09092","title":"iPUNet:Iterative Cross Field Guided Point Cloud Upsampling","created":"2023-10-13","cited_by_count":4,"area":null},{"id":"2312.03298","title":"DiffPMAE: Diffusion Masked Autoencoders for Point Cloud Reconstruction","created":"2023-12-06","cited_by_count":4,"area":null},{"id":"2408.02966","title":"Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement","created":"2024-08-06","cited_by_count":4,"area":null},{"id":"2411.00857","title":"Deep Learning for 3D Point Cloud Enhancement: A Survey","created":"2024-10-30","cited_by_count":4,"area":null},{"id":"2501.15286","title":"Efficient Point Clouds Upsampling via Flow Matching","created":"2025-01-25","cited_by_count":4,"area":"diffusion"},{"id":"2104.14769","title":"PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery","created":"2021-04-30","cited_by_count":3,"area":null},{"id":"2105.14548","title":"Z2P: Instant Visualization of Point Clouds","created":"2021-05-30","cited_by_count":3,"area":null},{"id":"2106.13765","title":"\"Zero-Shot\" Point Cloud Upsampling","created":"2021-06-25","cited_by_count":3,"area":null},{"id":"2112.11271","title":"High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling","created":"2021-12-21","cited_by_count":3,"area":null},{"id":"2307.08992","title":"Arbitrary point cloud upsampling via Dual Back-Projection Network","created":"2023-07-18","cited_by_count":3,"area":null},{"id":"2410.17001","title":"Joint Point Cloud Upsampling and Cleaning with Octree-based CNNs","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2510.25210","title":"U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching","created":"2025-10-29","cited_by_count":3,"area":null},{"id":"2107.02909","title":"Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks","created":"2021-07-02","cited_by_count":2,"area":null},{"id":"2202.08583","title":"Point cloud completion via structured feature maps using a feedback network","created":"2022-02-17","cited_by_count":2,"area":null},{"id":"2205.01458","title":"Frequency-Selective Geometry Upsampling of Point Clouds","created":"2022-05-03","cited_by_count":2,"area":null},{"id":"2206.12648","title":"BIMS-PU: Bi-Directional and Multi-Scale Point Cloud Upsampling","created":"2022-06-25","cited_by_count":2,"area":null},{"id":"2208.10968","title":"PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention","created":"2022-08-22","cited_by_count":2,"area":null},{"id":"2209.06112","title":"CU-Net: Real-Time High-Fidelity Color Upsampling for Point Clouds","created":"2022-09-12","cited_by_count":2,"area":null},{"id":"2305.01148","title":"PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud Upsampling","created":"2023-05-02","cited_by_count":2,"area":null},{"id":"2403.01663","title":"PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network","created":"2024-03-04","cited_by_count":2,"area":null},{"id":"2407.04476","title":"Rethinking Data Input for Point Cloud Upsampling","created":"2024-07-05","cited_by_count":2,"area":null},{"id":"2412.12208","title":"AI-Driven Innovations in Volumetric Video Streaming: A Review","created":"2024-12-15","cited_by_count":2,"area":null},{"id":"2503.09439","title":"SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2108.01411","title":"HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population","created":"2021-08-03","cited_by_count":1,"area":null},{"id":"2202.13577","title":"Point Set Self-Embedding","created":"2022-02-28","cited_by_count":1,"area":null},{"id":"2203.09088","title":"Deep Point Cloud Simplification for High-quality Surface Reconstruction","created":"2022-03-17","cited_by_count":1,"area":null},{"id":"2207.10279","title":"Gradient-based Point Cloud Denoising with Uniformity","created":"2022-07-21","cited_by_count":1,"area":null},{"id":"2303.08240","title":"Parametric Surface Constrained Upsampler Network for Point Cloud","created":"2023-03-14","cited_by_count":1,"area":null},{"id":"2306.01081","title":"4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks","created":"2023-06-01","cited_by_count":1,"area":null},{"id":"2310.08755","title":"PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface","created":"2023-10-12","cited_by_count":1,"area":null},{"id":"2409.01581","title":"GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting","created":"2024-09-03","cited_by_count":1,"area":null},{"id":"2410.15941","title":"MBPU: A Plug-and-Play State Space Model for Point Cloud Upsamping with Fast Point Rendering","created":"2024-10-21","cited_by_count":1,"area":"ssm"},{"id":"2411.00432","title":"PLATYPUS: Progressive Local Surface Estimator for Arbitrary-Scale Point Cloud Upsampling","created":"2024-11-01","cited_by_count":1,"area":null},{"id":"2501.07076","title":"Representation Learning of Point Cloud Upsampling in Global and Local Inputs","created":"2025-01-13","cited_by_count":1,"area":null},{"id":"2502.12151","title":"VoLUT: Efficient Volumetric streaming enhanced by LUT-based super-resolution","created":"2025-02-17","cited_by_count":1,"area":null},{"id":"2502.19452","title":"SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network","created":"2025-02-26","cited_by_count":1,"area":null},{"id":"2504.11701","title":"Non-uniform Point Cloud Upsampling via Local Manifold Distribution","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2509.20207","title":"PU-Gaussian: Point Cloud Upsampling using 3D Gaussian Representation","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2205.01087","title":"Incomplete Gamma Kernels: Generalizing Locally Optimal Projection Operators","created":"2022-05-02","cited_by_count":0,"area":null},{"id":"2205.09594","title":"A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling","created":"2022-05-19","cited_by_count":0,"area":null},{"id":"2210.03331","title":"Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling","created":"2022-10-07","cited_by_count":0,"area":null},{"id":"2301.11630","title":"Joint Geometry and Attribute Upsampling of Point Clouds Using Frequency-Selective Models with Overlapped Support","created":"2023-01-27","cited_by_count":0,"area":null},{"id":"2305.03216","title":"Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution","created":"2023-05-05","cited_by_count":0,"area":null},{"id":"2305.12646","title":"SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2404.01112","title":"Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion features","created":"2024-04-01","cited_by_count":0,"area":null},{"id":"2411.01734","title":"Next Best View For Point-Cloud Model Acquisition: Bayesian Approximation and Uncertainty Analysis","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2501.16716","title":"Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstruction","created":"2025-01-28","cited_by_count":0,"area":null},{"id":"2506.06864","title":"Face recognition on point cloud with cgan-top for denoising","created":"2025-06-07","cited_by_count":0,"area":null},{"id":"2506.22749","title":"Deep Learning based Joint Geometry and Attribute Up-sampling for Large-Scale Colored Point Clouds","created":"2025-06-28","cited_by_count":0,"area":null},{"id":"2507.19121","title":"Preserving Topological and Geometric Embeddings for Point Cloud Recovery","created":"2025-07-25","cited_by_count":0,"area":null},{"id":"2508.12986","title":"Point upsampling networks for single-photon sensing","created":"2025-08-18","cited_by_count":0,"area":null},{"id":"2508.17050","title":"PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models","created":"2025-08-23","cited_by_count":0,"area":"diffusion"},{"id":"2512.20988","title":"PUFM++: Point Cloud Upsampling via Enhanced Flow Matching","created":"2025-12-24","cited_by_count":0,"area":"diffusion"},{"id":"2605.02201","title":"Super-Resolution of Airborne Laser Scanning Point Clouds for Forest Inventory","created":"2026-05-04","cited_by_count":0,"area":null}],"next_cursor":"100"}