{"list":"other","total":52,"rows":[{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1912.04838","title":"Scalability in Perception for Autonomous Driving: Waymo Open Dataset","created":"2019-12-10","cited_by_count":2451},{"id":"1711.03938","title":"CARLA: An Open Urban Driving Simulator","created":"2017-11-10","cited_by_count":2449},{"id":"2006.10739","title":"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains","created":"2020-06-18","cited_by_count":2200},{"id":"2111.12077","title":"Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields","created":"2021-11-23","cited_by_count":2032},{"id":"2103.13415","title":"Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields","created":"2021-03-24","cited_by_count":1964},{"id":"2112.05131","title":"Plenoxels: Radiance Fields without Neural Networks","created":"2021-12-09","cited_by_count":1613},{"id":"2012.02190","title":"pixelNeRF: Neural Radiance Fields from One or Few Images","created":"2020-12-03","cited_by_count":1584},{"id":"1911.11763","title":"SuperGlue: Learning Feature Matching with Graph Neural Networks","created":"2019-11-26","cited_by_count":1443},{"id":"1712.07629","title":"SuperPoint: Self-Supervised Interest Point Detection and Description","created":"2017-12-20","cited_by_count":1320},{"id":"2111.11215","title":"Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction","created":"2021-11-22","cited_by_count":964},{"id":"2010.07492","title":"NeRF++: Analyzing and Improving Neural Radiance Fields","created":"2020-10-15","cited_by_count":861},{"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":"2112.00724","title":"RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs","created":"2021-12-01","cited_by_count":615},{"id":"2112.03907","title":"Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields","created":"2021-12-07","cited_by_count":575},{"id":"1812.03506","title":"From Coarse to Fine: Robust Hierarchical Localization at Large Scale","created":"2018-12-09","cited_by_count":559},{"id":"2104.10133","title":"Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset","created":"2021-04-20","cited_by_count":537},{"id":"2012.03927","title":"NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis","created":"2020-12-07","cited_by_count":496},{"id":"2104.00677","title":"Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis","created":"2021-04-01","cited_by_count":464},{"id":"2012.03918","title":"NeRD: Neural Reflectance Decomposition from Image Collections","created":"2020-12-07","cited_by_count":449},{"id":"1712.04143","title":"Benchmarking Single Image Dehazing and Beyond","created":"2017-12-12","cited_by_count":447},{"id":"1708.07819","title":"Semantic Foggy Scene Understanding with Synthetic Data","created":"2017-08-25","cited_by_count":403},{"id":"2112.10703","title":"Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs","created":"2021-12-20","cited_by_count":398},{"id":"2112.05139","title":"CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields","created":"2021-12-09","cited_by_count":370},{"id":"1711.07064","title":"DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks","created":"2017-11-19","cited_by_count":367},{"id":"2112.03288","title":"Dense Depth Priors for Neural Radiance Fields from Sparse Input Views","created":"2021-12-06","cited_by_count":302},{"id":"2011.10379","title":"Neural Scene Graphs for Dynamic Scenes","created":"2020-11-20","cited_by_count":298},{"id":"2205.04334","title":"Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation","created":"2022-05-09","cited_by_count":243},{"id":"2103.03231","title":"DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks","created":"2021-03-04","cited_by_count":232},{"id":"2205.04978","title":"NeRF-Editing: Geometry Editing of Neural Radiance Fields","created":"2022-05-10","cited_by_count":217},{"id":"1708.04512","title":"DesnowNet: Context-Aware Deep Network for Snow Removal","created":"2017-08-15","cited_by_count":200},{"id":"2203.11483","title":"Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation","created":"2022-03-22","cited_by_count":197},{"id":"2112.15399","title":"InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering","created":"2021-12-31","cited_by_count":182},{"id":"2204.00987","title":"BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation","created":"2022-04-03","cited_by_count":168},{"id":"1609.02087","title":"Clearing the Skies: A deep network architecture for single-image rain removal","created":"2016-09-07","cited_by_count":136},{"id":"2203.14211","title":"DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation","created":"2022-03-27","cited_by_count":118},{"id":"1804.05101","title":"O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images","created":"2018-04-13","cited_by_count":95},{"id":"2205.15768","title":"SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections","created":"2022-05-31","cited_by_count":89},{"id":"2207.12298","title":"Deforming Radiance Fields with Cages","created":"2022-07-25","cited_by_count":78},{"id":"2304.07743","title":"SeaThru-NeRF: Neural Radiance Fields in Scattering Media","created":"2023-04-16","cited_by_count":75},{"id":"2207.09686","title":"Object-Compositional Neural Implicit Surfaces","created":"2022-07-20","cited_by_count":74},{"id":"2009.03683","title":"Rain rendering for evaluating and improving robustness to bad weather","created":"2020-09-06","cited_by_count":57},{"id":"1908.10335","title":"Physics-Based Rendering for Improving Robustness to Rain","created":"2019-08-27","cited_by_count":49},{"id":"2207.01164","title":"Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations","created":"2022-07-04","cited_by_count":46},{"id":"2209.13091","title":"WaterNeRF: Neural Radiance Fields for Underwater Scenes","created":"2022-09-27","cited_by_count":29},{"id":"2303.09757","title":"Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior","created":"2023-03-17","cited_by_count":22},{"id":"1906.08953","title":"Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios","created":"2019-06-21","cited_by_count":12},{"id":"2110.12993","title":"Neural Relightable Participating Media Rendering","created":"2021-10-25","cited_by_count":12},{"id":"1901.05946","title":"Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation","created":"2019-01-17","cited_by_count":4},{"id":"2102.05847","title":"ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media","created":"2021-02-11","cited_by_count":4}],"next_cursor":null}