358 citations · 387 across the 27 of their papers we have counts for
6 papers · 1 filter
MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data
Hanwen Jiang, Zexiang Xu, Desai Xie +11
We propose scaling up 3D scene reconstruction by training with synthesized data. At the core of our work is MegaSynth, a procedurally generated 3D dataset comprising 700K scenes -…
LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
Haian Jin, Hanwen Jiang, Hao Tan +6
We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two a…
Atlas Gaussians Diffusion for 3D Generation
Haitao Yang, Yuan Dong, Hanwen Jiang +3
Using the latent diffusion model has proven effective in developing novel 3D generation techniques. To harness the latent diffusion model, a key challenge is designing a high-fidel…
Real3D: Scaling Up Large Reconstruction Models with Real-World Images
Hanwen Jiang, Qixing Huang, Georgios Pavlakos
The default strategy for training single-view Large Reconstruction Models (LRMs) follows the fully supervised route using large-scale datasets of synthetic 3D assets or multi-view…
CoFie: Learning Compact Neural Surface Representations with Coordinate Fields
Hanwen Jiang, Haitao Yang, Georgios Pavlakos +1
This paper introduces CoFie, a novel local geometry-aware neural surface representation. CoFie is motivated by the theoretical analysis of local SDFs with quadratic approximation.…
OmniGlue: Generalizable Feature Matching with Foundation Model Guidance
Hanwen Jiang, Arjun Karpur, Bingyi Cao +2
The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques, with ever-improving performance on conventional benchmarks. Howe…