{"list":"other","total":69,"rows":[{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2308.04079","title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","created":"2023-08-08","cited_by_count":6226},{"id":"2212.09748","title":"Scalable Diffusion Models with Transformers","created":"2022-12-19","cited_by_count":6054},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"2209.03003","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","created":"2022-09-07","cited_by_count":3700},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"id":"1512.03012","title":"ShapeNet: An Information-Rich 3D Model Repository","created":"2015-12-09","cited_by_count":2897},{"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":"2303.01469","title":"Consistency Models","created":"2023-03-02","cited_by_count":1822},{"id":"2508.10104","title":"DINOv3","created":"2025-08-13","cited_by_count":1414},{"id":"2311.04400","title":"LRM: Large Reconstruction Model for Single Image to 3D","created":"2023-11-08","cited_by_count":849},{"id":"2412.01506","title":"Structured 3D Latents for Scalable and Versatile 3D Generation","created":"2024-12-02","cited_by_count":770},{"id":"2103.01458","title":"Diffusion Probabilistic Models for 3D Point Cloud Generation","created":"2021-03-02","cited_by_count":735},{"id":"2212.08751","title":"Point-E: A System for Generating 3D Point Clouds from Complex Prompts","created":"2022-12-16","cited_by_count":699},{"id":"1804.01654","title":"Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images","created":"2018-04-05","cited_by_count":651},{"id":"1604.00449","title":"3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction","created":"2016-04-02","cited_by_count":588},{"id":"2505.13447","title":"Mean Flows for One-step Generative Modeling","created":"2025-05-19","cited_by_count":561},{"id":"2210.06978","title":"LION: Latent Point Diffusion Models for 3D Shape Generation","created":"2022-10-12","cited_by_count":553},{"id":"1802.05384","title":"AtlasNet: A Papier-M\\^ach\\'e Approach to Learning 3D Surface Generation","created":"2018-02-15","cited_by_count":539},{"id":"2201.08845","title":"Point-NeRF: Point-based Neural Radiance Fields","created":"2022-01-21","cited_by_count":503},{"id":"2310.02279","title":"Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion","created":"2023-10-01","cited_by_count":459},{"id":"2310.14189","title":"Improved Techniques for Training Consistency Models","created":"2023-10-22","cited_by_count":439},{"id":"2410.12557","title":"One Step Diffusion via Shortcut Models","created":"2024-10-16","cited_by_count":391},{"id":"2305.02463","title":"Shap-E: Generating Conditional 3D Implicit Functions","created":"2023-05-03","cited_by_count":385},{"id":"2410.11081","title":"Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models","created":"2024-10-14","cited_by_count":283},{"id":"2511.16624","title":"SAM 3D: 3Dfy Anything in Images","created":"2025-11-20","cited_by_count":247},{"id":"1804.04610","title":"Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling","created":"2018-04-12","cited_by_count":218},{"id":"2403.05034","title":"CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model","created":"2024-03-08","cited_by_count":215},{"id":"2003.10432","title":"Atlas: End-to-End 3D Scene Reconstruction from Posed Images","created":"2020-03-23","cited_by_count":206},{"id":"2304.06714","title":"Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction","created":"2023-04-13","cited_by_count":180},{"id":"2512.14692","title":"Native and Compact Structured Latents for 3D Generation","created":"2025-12-16","cited_by_count":175},{"id":"2406.14548","title":"Consistency Models Made Easy","created":"2024-06-20","cited_by_count":144},{"id":"2211.13757","title":"Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions","created":"2022-11-24","cited_by_count":133},{"id":"2503.07565","title":"Inductive Moment Matching","created":"2025-03-10","cited_by_count":113},{"id":"2307.01831","title":"DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation","created":"2023-07-04","cited_by_count":110},{"id":"2512.02012","title":"Improved Mean Flows: On the Challenges of Fastforward Generative Models","created":"2025-12-01","cited_by_count":110},{"id":"2405.20320","title":"Improving the Training of Rectified Flows","created":"2024-05-30","cited_by_count":108},{"id":"2201.01831","title":"POCO: Point Convolution for Surface Reconstruction","created":"2022-01-05","cited_by_count":92},{"id":"2403.12019","title":"LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation","created":"2024-03-18","cited_by_count":92},{"id":"1810.09381","title":"Unsupervised Learning of Shape and Pose with Differentiable Point Clouds","created":"2018-10-22","cited_by_count":89},{"id":"2302.10668","title":"$PC^2$: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction","created":"2023-02-21","cited_by_count":84},{"id":"2503.21732","title":"SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling","created":"2025-03-27","cited_by_count":79},{"id":"2408.00653","title":"SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement","created":"2024-08-01","cited_by_count":65},{"id":"2601.22158","title":"One-step Latent-free Image Generation with Pixel Mean Flows","created":"2026-01-29","cited_by_count":53},{"id":"2512.03052","title":"LATTICE: Democratize High-Fidelity 3D Generation at Scale","created":"2025-11-24","cited_by_count":44},{"id":"2501.04689","title":"SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images","created":"2025-01-08","cited_by_count":42},{"id":"2510.20771","title":"AlphaFlow: Understanding and Improving MeanFlow Models","created":"2025-10-23","cited_by_count":41},{"id":"2312.14198","title":"ZeroShape: Regression-based Zero-shot Shape Reconstruction","created":"2023-12-21","cited_by_count":30},{"id":"2106.12102","title":"LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction","created":"2021-06-23","cited_by_count":25},{"id":"2006.07752","title":"3D Reconstruction of Novel Object Shapes from Single Images","created":"2020-06-14","cited_by_count":21},{"id":"2510.24474","title":"Decoupled MeanFlow: Turning Flow Models into Flow Maps for Accelerated Sampling","created":"2025-10-28","cited_by_count":20},{"id":"2403.06973","title":"Bayesian Diffusion Models for 3D Shape Reconstruction","created":"2024-03-11","cited_by_count":19},{"id":"2005.01939","title":"From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks","created":"2020-05-05","cited_by_count":18},{"id":"2407.04237","title":"GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction","created":"2024-07-05","cited_by_count":18},{"id":"2512.02636","title":"Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models","created":"2025-12-02","cited_by_count":18},{"id":"2304.06247","title":"ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-based Consistency","created":"2023-04-13","cited_by_count":16},{"id":"2512.21185","title":"UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement","created":"2025-12-24","cited_by_count":16},{"id":"2404.03566","title":"PointInfinity: Resolution-Invariant Point Diffusion Models","created":"2024-04-04","cited_by_count":15},{"id":"2401.02414","title":"Bring Metric Functions into Diffusion Models","created":"2024-01-04","cited_by_count":7},{"id":"2403.14539","title":"Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild","created":"2024-03-21","cited_by_count":7},{"id":"2407.14979","title":"RGB2Point: 3D Point Cloud Generation from Single RGB Images","created":"2024-07-20","cited_by_count":7},{"id":"2509.08104","title":"APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction","created":"2025-09-09","cited_by_count":3},{"id":"2511.06310","title":"Adaptive 3D Reconstruction via Diffusion Priors and Forward Curvature-Matching Likelihood Updates","created":"2025-11-09","cited_by_count":1}],"next_cursor":null}