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Found 8 papers, 5 papers with code
Learning Generalizable Feature Fields for Mobile Manipulation
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects.
Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models
Specifically, we decompose the NVS task into two stages: (i) transforming observed regions to a novel view, and (ii) hallucinating unseen regions.
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model ($\textit{e. g.}$, Stable Diffusion) to distill rich semantic information into the deep 3D voxel.
MVDream: Multi-view Diffusion for 3D Generation
We introduce MVDream, a diffusion model that is able to generate consistent multi-view images from a given text prompt.
FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models
Recent works on generalizable NeRFs have shown promising results on novel view synthesis from single or few images.
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF.
GIFS: Neural Implicit Function for General Shape Representation
This limitation leads to tedious data processing (converting non-watertight raw data to watertight) as well as the incapability of representing general object shapes in the real world.
Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild
Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems.
