{"total":21,"rows":[{"id":"2511.10647","title":"Depth Anything 3: Recovering the Visual Space from Any Views","created":"2025-11-13","cited_by_count":692,"area":null},{"id":"2505.00702","title":"RayZer: A Self-supervised Large View Synthesis Model","created":"2025-05-01","cited_by_count":77,"area":null},{"id":"2507.14501","title":"Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey","created":"2025-07-19","cited_by_count":27,"area":null},{"id":"2605.15195","title":"VGGT-$\\Omega$","created":"2026-05-14","cited_by_count":26,"area":null},{"id":"2506.09997","title":"DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos","created":"2025-06-11","cited_by_count":15,"area":null},{"id":"2604.04931","title":"LoMa: Local Feature Matching Revisited","created":"2026-04-06","cited_by_count":11,"area":null},{"id":"2506.03118","title":"HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers","created":"2025-06-03","cited_by_count":9,"area":null},{"id":"2607.06560","title":"Vision as Unified Multimodal Generation","created":"2026-07-07","cited_by_count":7,"area":null},{"id":"2512.13683","title":"I-Scene: 3D Instance Models are Implicit Generalizable Spatial Learners","created":"2025-12-15","cited_by_count":6,"area":null},{"id":"2509.06950","title":"Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data","created":"2025-09-08","cited_by_count":4,"area":null},{"id":"2603.21064","title":"2Xplat: Decoupling Geometry and Appearance Modeling for Feed-Forward 3D Gaussian Splatting","created":"2026-03-22","cited_by_count":2,"area":null},{"id":"2604.04925","title":"SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo","created":"2026-04-06","cited_by_count":2,"area":null},{"id":"2604.09411","title":"SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data","created":"2026-04-10","cited_by_count":2,"area":null},{"id":"2609.34621","title":"Does Native 3D Texture Generation Necessarily Require 3D Assets for Training?","created":"2026-09-28","cited_by_count":2,"area":null},{"id":"2604.10512","title":"FreeScale: Scaling 3D Scenes via Certainty-Aware Free-View Generation","created":"2026-04-12","cited_by_count":1,"area":null},{"id":"2604.11809","title":"Who Handles Orientation? Investigating Invariance in Feature Matching","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2606.31585","title":"DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2511.03970","title":"Room Envelopes: A Synthetic Dataset for Indoor Layout Reconstruction from Images","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2604.10095","title":"Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models","created":"2026-04-11","cited_by_count":0,"area":null},{"id":"2605.22020","title":"ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2609.09507","title":"RoMa-$\\Omega$: What Feed-Forward 3D Models Know About Image Matching","created":"2026-09-08","cited_by_count":0,"area":null}],"next_cursor":null}