{"list":"other","total":73,"rows":[{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"2201.05989","title":"Instant Neural Graphics Primitives with a Multiresolution Hash Encoding","created":"2022-01-16","cited_by_count":3723},{"id":"2003.12039","title":"RAFT: Recurrent All-Pairs Field Transforms for Optical Flow","created":"2020-03-26","cited_by_count":2415},{"id":"2103.13415","title":"Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields","created":"2021-03-24","cited_by_count":1964},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"2112.05131","title":"Plenoxels: Radiance Fields without Neural Networks","created":"2021-12-09","cited_by_count":1613},{"id":"1703.06211","title":"Deformable Convolutional Networks","created":"2017-03-17","cited_by_count":1488},{"id":"2011.13961","title":"D-NeRF: Neural Radiance Fields for Dynamic Scenes","created":"2020-11-27","cited_by_count":1465},{"id":"2203.09517","title":"TensoRF: Tensorial Radiance Fields","created":"2022-03-17","cited_by_count":1280},{"id":"1809.00219","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","created":"2018-09-01","cited_by_count":1254},{"id":"2008.02268","title":"NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections","created":"2020-08-05","cited_by_count":1232},{"id":"1501.00092","title":"Image Super-Resolution Using Deep Convolutional Networks","created":"2014-12-31","cited_by_count":1224},{"id":"2106.13230","title":"Video Swin Transformer","created":"2021-06-24","cited_by_count":1091},{"id":"2107.10833","title":"Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data","created":"2021-07-22","cited_by_count":970},{"id":"2111.11215","title":"Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction","created":"2021-11-22","cited_by_count":964},{"id":"2103.14024","title":"PlenOctrees for Real-time Rendering of Neural Radiance Fields","created":"2021-03-25","cited_by_count":905},{"id":"2010.07492","title":"NeRF++: Analyzing and Improving Neural Radiance Fields","created":"2020-10-15","cited_by_count":861},{"id":"1802.08797","title":"Residual Dense Network for Image Super-Resolution","created":"2018-02-24","cited_by_count":799},{"id":"2103.15595","title":"MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo","created":"2021-03-29","cited_by_count":740},{"id":"2107.02791","title":"Depth-supervised NeRF: Fewer Views and Faster Training for Free","created":"2021-07-06","cited_by_count":735},{"id":"2202.05263","title":"Block-NeRF: Scalable Large Scene Neural View Synthesis","created":"2022-02-10","cited_by_count":724},{"id":"2102.13090","title":"IBRNet: Learning Multi-View Image-Based Rendering","created":"2021-02-25","cited_by_count":703},{"id":"2103.13744","title":"KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs","created":"2021-03-25","cited_by_count":673},{"id":"1905.00889","title":"Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines","created":"2019-05-02","cited_by_count":672},{"id":"2104.06405","title":"BARF: Bundle-Adjusting Neural Radiance Fields","created":"2021-04-13","cited_by_count":642},{"id":"1711.09078","title":"Video Enhancement with Task-Oriented Flow","created":"2017-11-24","cited_by_count":619},{"id":"2112.00724","title":"RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs","created":"2021-12-01","cited_by_count":615},{"id":"2103.14006","title":"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution","created":"2021-03-25","cited_by_count":529},{"id":"2103.10380","title":"FastNeRF: High-Fidelity Neural Rendering at 200FPS","created":"2021-03-18","cited_by_count":511},{"id":"2201.08845","title":"Point-NeRF: Point-based Neural Radiance Fields","created":"2022-01-21","cited_by_count":503},{"id":"1905.02716","title":"EDVR: Video Restoration with Enhanced Deformable Convolutional Networks","created":"2019-05-07","cited_by_count":494},{"id":"1611.00850","title":"Optical Flow Estimation using a Spatial Pyramid Network","created":"2016-11-03","cited_by_count":424},{"id":"2104.13371","title":"BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment","created":"2021-04-27","cited_by_count":342},{"id":"2111.13679","title":"NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images","created":"2021-11-26","cited_by_count":336},{"id":"2104.00674","title":"PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting","created":"2021-04-01","cited_by_count":335},{"id":"2208.00277","title":"MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures","created":"2022-07-30","cited_by_count":311},{"id":"2012.02181","title":"BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond","created":"2020-12-03","cited_by_count":304},{"id":"2003.10428","title":"Deep Unfolding Network for Image Super-Resolution","created":"2020-03-23","cited_by_count":254},{"id":"2112.05504","title":"BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering","created":"2021-12-10","cited_by_count":229},{"id":"1812.02898","title":"TDAN: Temporally Deformable Alignment Network for Video Super-Resolution","created":"2018-12-07","cited_by_count":226},{"id":"2201.12288","title":"VRT: A Video Restoration Transformer","created":"2022-01-28","cited_by_count":220},{"id":"2108.13826","title":"Self-Calibrating Neural Radiance Fields","created":"2021-08-31","cited_by_count":204},{"id":"2111.13539","title":"GeoNeRF: Generalizing NeRF with Geometry Priors","created":"2021-11-26","cited_by_count":196},{"id":"2204.06837","title":"Modeling Indirect Illumination for Inverse Rendering","created":"2022-04-14","cited_by_count":168},{"id":"2011.12490","title":"DeRF: Decomposed Radiance Fields","created":"2020-11-25","cited_by_count":163},{"id":"1903.09814","title":"Feedback Network for Image Super-Resolution","created":"2019-03-23","cited_by_count":155},{"id":"2102.07064","title":"NeRF--: Neural Radiance Fields Without Known Camera Parameters","created":"2021-02-14","cited_by_count":155},{"id":"2111.12704","title":"Investigating Tradeoffs in Real-World Video Super-Resolution","created":"2021-11-24","cited_by_count":134},{"id":"2111.15234","title":"NeRFReN: Neural Radiance Fields with Reflections","created":"2021-11-30","cited_by_count":122},{"id":"2110.14217","title":"Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects","created":"2021-10-27","cited_by_count":119},{"id":"2206.00878","title":"EfficientNeRF: Efficient Neural Radiance Fields","created":"2022-06-02","cited_by_count":111},{"id":"2112.09687","title":"Light Field Neural Rendering","created":"2021-12-17","cited_by_count":108},{"id":"2103.00762","title":"NeuTex: Neural Texture Mapping for Volumetric Neural Rendering","created":"2021-03-01","cited_by_count":98},{"id":"2112.01759","title":"NeRF-SR: High-Quality Neural Radiance Fields using Supersampling","created":"2021-12-03","cited_by_count":95},{"id":"2007.11803","title":"MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution","created":"2020-07-23","cited_by_count":89},{"id":"2105.10422","title":"LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond","created":"2021-05-21","cited_by_count":88},{"id":"2207.10312","title":"AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields","created":"2022-07-21","cited_by_count":68},{"id":"2111.10427","title":"DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering","created":"2021-11-19","cited_by_count":60},{"id":"2207.11938","title":"Reference-based Image Super-Resolution with Deformable Attention Transformer","created":"2022-07-25","cited_by_count":51},{"id":"2210.12782","title":"Compressing Explicit Voxel Grid Representations: fast NeRFs become also small","created":"2022-10-23","cited_by_count":48},{"id":"1904.10343","title":"Path-Restore: Learning Network Path Selection for Image Restoration","created":"2019-04-23","cited_by_count":40},{"id":"2208.06821","title":"Fast Learning Radiance Fields by Shooting Much Fewer Rays","created":"2022-08-14","cited_by_count":38},{"id":"2105.09103","title":"Recursive-NeRF: An Efficient and Dynamically Growing NeRF","created":"2021-05-19","cited_by_count":32},{"id":"2103.15295","title":"Best-Buddy GANs for Highly Detailed Image Super-Resolution","created":"2021-03-29","cited_by_count":30},{"id":"2303.06919","title":"NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer","created":"2023-03-13","cited_by_count":29},{"id":"2108.04886","title":"Differentiable Surface Rendering via Non-Differentiable Sampling","created":"2021-08-10","cited_by_count":28},{"id":"2208.00945","title":"DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields","created":"2022-08-01","cited_by_count":26},{"id":"1809.08573","title":"Learning for Video Super-Resolution through HR Optical Flow Estimation","created":"2018-09-23","cited_by_count":24},{"id":"2111.15288","title":"Revisiting Temporal Alignment for Video Restoration","created":"2021-11-30","cited_by_count":24},{"id":"2212.04701","title":"4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions","created":"2022-12-09","cited_by_count":23},{"id":"2207.02621","title":"VMRF: View Matching Neural Radiance Fields","created":"2022-07-06","cited_by_count":17},{"id":"2204.08023","title":"VDTR: Video Deblurring with Transformer","created":"2022-04-17","cited_by_count":15}],"next_cursor":null}