Search Results for author:
Found 9 papers, 6 papers with code
Cameras as Rays: Pose Estimation via Ray Diffusion
Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views (<10).
RelPose++: Recovering 6D Poses from Sparse-view Observations
We address the task of estimating 6D camera poses from sparse-view image sets (2-8 images).
SUDS: Scalable Urban Dynamic Scenes
We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes.
SparsePose: Sparse-View Camera Pose Regression and Refinement
Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene.
RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild
We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object.
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
NeRS learns a neural shape representation of a closed surface that is diffeomorphic to a sphere, guaranteeing water-tight reconstructions.
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment.
Ranked #3 on
3D Object Reconstruction
on BEHAVE
Predicting 3D Human Dynamics from Video
In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input.
Learning 3D Human Dynamics from Video
We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features.
Ranked #15 on
3D Human Pose Estimation
on 3DPW
(Acceleration Error metric)
