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Found 10 papers, 5 papers with code
Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf.
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields
This formalism is implemented in three steps: assigning a consistent local coordinate frame to the task-relevant object parts, determining the location and orientation of this coordinate frame on unseen object instances, and executing an action that brings these frames into the desired alignment.
Vision Transformer for NeRF-Based View Synthesis from a Single Input Image
Existing approaches condition on local image features to reconstruct a 3D object, but often render blurry predictions at viewpoints that are far away from the source view.
NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields
In particular, we demonstrate that a NeRF representation of a scene can be used to train dense object descriptors.
CLA-NeRF: Category-Level Articulated Neural Radiance Field
We propose CLA-NeRF -- a Category-Level Articulated Neural Radiance Field that can perform view synthesis, part segmentation, and articulated pose estimation.
Learning to See before Learning to Act: Visual Pre-training for Manipulation
With just a small amount of robotic experience, we can further fine-tune the affordance model to achieve better results.
Neural Volume Rendering: NeRF And Beyond
Besides the COVID-19 pandemic and political upheaval in the US, 2020 was also the year in which neural volume rendering exploded onto the scene, triggered by the impressive NeRF paper by Mildenhall et al. (2020).
INeRF: Inverting Neural Radiance Fields for Pose Estimation
We then show that for complex real-world scenes from the LLFF dataset, iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF.
Debiased Contrastive Learning
A prominent technique for self-supervised representation learning has been to contrast semantically similar and dissimilar pairs of samples.
Experience-Embedded Visual Foresight
In this paper, we tackle the generalization problem via fast adaptation, where we train a prediction model to quickly adapt to the observed visual dynamics of a novel object.
