{"list":"other","total":55,"rows":[{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2201.05989","title":"Instant Neural Graphics Primitives with a Multiresolution Hash Encoding","created":"2022-01-16","cited_by_count":3723},{"id":"1901.05103","title":"DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation","created":"2019-01-16","cited_by_count":2689},{"id":"1812.03828","title":"Occupancy Networks: Learning 3D Reconstruction in Function Space","created":"2018-12-10","cited_by_count":2041},{"id":"2106.10689","title":"NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction","created":"2021-06-20","cited_by_count":1712},{"id":"2112.05131","title":"Plenoxels: Radiance Fields without Neural Networks","created":"2021-12-09","cited_by_count":1613},{"id":"2106.12052","title":"Volume Rendering of Neural Implicit Surfaces","created":"2021-06-22","cited_by_count":996},{"id":"1906.01618","title":"Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations","created":"2019-06-04","cited_by_count":864},{"id":"2007.08501","title":"Accelerating 3D Deep Learning with PyTorch3D","created":"2020-07-16","cited_by_count":784},{"id":"1711.00199","title":"PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes","created":"2017-11-01","cited_by_count":775},{"id":"1801.09847","title":"Open3D: A Modern Library for 3D Data Processing","created":"2018-01-30","cited_by_count":721},{"id":"2002.10099","title":"Implicit Geometric Regularization for Learning Shapes","created":"2020-02-24","cited_by_count":710},{"id":"1912.07372","title":"Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision","created":"2019-12-16","cited_by_count":700},{"id":"2310.15008","title":"Wonder3D: Single Image to 3D using Cross-Domain Diffusion","created":"2023-10-23","cited_by_count":689},{"id":"2104.10078","title":"UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction","created":"2021-04-20","cited_by_count":646},{"id":"2204.11918","title":"Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items","created":"2022-04-25","cited_by_count":612},{"id":"2302.04264","title":"Nerfstudio: A Modular Framework for Neural Radiance Field Development","created":"2023-02-08","cited_by_count":600},{"id":"1901.02970","title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","created":"2019-01-09","cited_by_count":495},{"id":"2206.00665","title":"MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction","created":"2022-06-01","cited_by_count":470},{"id":"2306.03092","title":"Neuralangelo: High-Fidelity Neural Surface Reconstruction","created":"2023-06-05","cited_by_count":467},{"id":"2312.08344","title":"FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects","created":"2023-12-13","cited_by_count":429},{"id":"2008.08465","title":"CosyPose: Consistent multi-view multi-object 6D pose estimation","created":"2020-08-19","cited_by_count":287},{"id":"1804.00175","title":"DeepIM: Deep Iterative Matching for 6D Pose Estimation","created":"2018-03-31","cited_by_count":283},{"id":"1808.08319","title":"BOP: Benchmark for 6D Object Pose Estimation","created":"2018-08-24","cited_by_count":254},{"id":"1809.10790","title":"Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects","created":"2018-09-27","cited_by_count":241},{"id":"1703.10896","title":"BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth","created":"2017-03-31","cited_by_count":225},{"id":"2302.14859","title":"BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis","created":"2023-02-28","cited_by_count":221},{"id":"1701.05498","title":"T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects","created":"2017-01-19","cited_by_count":210},{"id":"2212.06870","title":"MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare","created":"2022-12-13","cited_by_count":201},{"id":"2009.07378","title":"BOP Challenge 2020 on 6D Object Localization","created":"2020-09-15","cited_by_count":193},{"id":"1906.07751","title":"Neural Volumes: Learning Dynamic Renderable Volumes from Images","created":"2019-06-18","cited_by_count":173},{"id":"2205.12257","title":"OnePose: One-Shot Object Pose Estimation without CAD Models","created":"2022-05-24","cited_by_count":157},{"id":"1903.06684","title":"kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation","created":"2019-03-15","cited_by_count":153},{"id":"2004.00605","title":"EPOS: Estimating 6D Pose of Objects with Symmetries","created":"2020-04-01","cited_by_count":148},{"id":"2204.10776","title":"Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images","created":"2022-04-22","cited_by_count":121},{"id":"2311.15707","title":"SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation","created":"2023-11-27","cited_by_count":117},{"id":"2301.07673","title":"OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models","created":"2023-01-18","cited_by_count":115},{"id":"2311.14155","title":"GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence","created":"2023-11-23","cited_by_count":114},{"id":"1904.03167","title":"HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects","created":"2019-04-05","cited_by_count":110},{"id":"2311.18809","title":"FoundPose: Unseen Object Pose Estimation with Foundation Features","created":"2023-11-30","cited_by_count":109},{"id":"2008.08145","title":"Category Level Object Pose Estimation via Neural Analysis-by-Synthesis","created":"2020-08-18","cited_by_count":100},{"id":"2111.13489","title":"SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings","created":"2021-11-26","cited_by_count":95},{"id":"1912.00416","title":"LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation","created":"2019-12-01","cited_by_count":89},{"id":"2203.05701","title":"6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark","created":"2022-03-11","cited_by_count":86},{"id":"2203.14628","title":"FS6D: Few-Shot 6D Pose Estimation of Novel Objects","created":"2022-03-28","cited_by_count":81},{"id":"2203.15533","title":"OSOP: A Multi-Stage One Shot Object Pose Estimation Framework","created":"2022-03-29","cited_by_count":81},{"id":"1512.07506","title":"Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd","created":"2015-12-23","cited_by_count":79},{"id":"1502.05908","title":"Learning Descriptors for Object Recognition and 3D Pose Estimation","created":"2015-02-20","cited_by_count":60},{"id":"2312.00947","title":"FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models","created":"2023-12-01","cited_by_count":57},{"id":"2109.06161","title":"Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image","created":"2021-09-13","cited_by_count":42},{"id":"1509.01277","title":"A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place","created":"2015-09-03","cited_by_count":35},{"id":"2403.11510","title":"GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects","created":"2024-03-18","cited_by_count":33},{"id":"1607.08371","title":"Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study","created":"2016-07-28","cited_by_count":29},{"id":"1711.04061","title":"Going Further with Point Pair Features","created":"2017-11-11","cited_by_count":28},{"id":"2212.12902","title":"TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation","created":"2022-12-25","cited_by_count":23}],"next_cursor":null}