Search Results for author:
Found 8 papers, 3 papers with code
Removing Objects From Neural Radiance Fields
We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.
Map-free Visual Relocalization: Metric Pose Relative to a Single Image
Can we relocalize in a scene represented by a single reference image?
Learning Structured Gaussians to Approximate Deep Ensembles
Similarly to distillation approaches, our single network is trained to maximise the probability of samples from pre-trained probabilistic models, in this work we use a fixed ensemble of networks.
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Deep neural networks have recently been used to edit images with great success, in particular for faces.
Training VAEs Under Structured Residuals
This paper demonstrates a novel scheme to incorporate a structured Gaussian likelihood prediction network within the VAE that allows the residual correlations to be modeled.
Structured Uncertainty Prediction Networks
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image.
Lifting Object Detection Datasets into 3D
In particular, acquiring ground truth 3D shapes of objects pictured in 2D images remains a challenging feat and this has hampered progress in recognition-based object reconstruction from a single image.
Reconstructing PASCAL VOC
We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations.
