Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Estimating the pose of an uncooperative spacecraft is an important computer vision problem
for enabling the deployment of automatic vision-based systems in orbit, with applications …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

We present Neural Feature Fusion Fields (N3F),\a method that improves dense 2D image
feature extractors when the latter are applied to the analysis of multiple images …

We study the use of deep features extracted from a pretrained Vision Transformer (ViT) as
dense visual descriptors. We observe and empirically demonstrate that such features, when …

Humans naturally decompose their environment into entities at the appropriate level of
abstraction to act in the world. Allowing machine learning algorithms to derive this …

Unsupervised object-centric learning aims to represent the modular, compositional, and
causal structure of a scene as a set of object representations and thereby promises to …

Semantic segmentation has a broad range of applications, but its real-world impact has
been significantly limited by the prohibitive annotation costs necessary to enable …

Unsupervised multi-object segmentation has shown impressive results on images by
utilizing powerful semantics learned from self-supervised pretraining. An additional modality …

Recent works on generalizable NeRFs have shown promising results on novel view
synthesis from single or few images. However, such models have rarely been applied on …