3 citations · 3 across the 6 of their papers we have counts for
6 papers
CityRAG: Stepping Into a City via Spatially-Grounded Video Generation
Gene Chou, Charles Herrmann, Kyle Genova +6
We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video generative models can produc…
DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion
Qitao Zhao, Amy Lin, Jeff Tan +3
Current Structure-from-Motion (SfM) methods typically follow a two-stage pipeline, combining learned or geometric pairwise reasoning with a subsequent global optimization step. In…
Bolt3D: Generating 3D Scenes in Seconds
Stanislaw Szymanowicz, Jason Y. Zhang, Pratul Srinivasan +6
We present a latent diffusion model for fast feed-forward 3D scene generation. Given one or more images, our model Bolt3D directly samples a 3D scene representation in less than se…
UnCommon Objects in 3D
Xingchen Liu, Piyush Tayal, Jianyuan Wang +10
We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-reso…
Can Generative Video Models Help Pose Estimation?
Ruojin Cai, Jason Y. Zhang, Philipp Henzler +3
Pairwise pose estimation from images with little or no overlap is an open challenge in computer vision. Existing methods, even those trained on large-scale datasets, struggle in th…
Cameras as Rays: Pose Estimation via Ray Diffusion
Jason Y. Zhang, Amy Lin, Moneish Kumar +3
Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views (<10). In contrast to existing approaches that pursue top-d…