Dataset condensation aims at reducing the network training effort through condensing a
cumbersome training set into a compact synthetic one. State-of-the-art approaches largely …

The great success of deep learning heavily relies on increasingly larger training data, which
comes at a price of huge computational and infrastructural costs. This poses crucial …

The performance of existing single-view 3D reconstruction methods heavily relies on large-
scale 3D annotations. However, such annotations are tedious and expensive to collect …

A more realistic object detection paradigm, Open-World Object Detection, has arisen
increasing research interests in the community recently. A qualified open-world object …

Abstract 3D reconstruction of novel categories based on few-shot learning is appealing in
real-world applications and attracts increasing research interests. Previous approaches …

Three-dimensional reconstruction is a multimedia technology widely used in computer-
aided modeling and 3D animation. Nevertheless, it is still hard for reconstruction methods to …

A major gap between few-shot and many-shot learning is the data distribution empirically
oserved by the model during training. In few-shot learning, the learned model can easily …

We focus on enabling damage and tampering detection in logistics and tackle the problem
of 3D shape reconstruction of potentially damaged parcels. As input we utilize single RGB …

We present GSD, a diffusion model approach based on Gaussian Splatting (GS)
representation for 3D object reconstruction from a single view. Prior works suffer from …

Previous research in multi-view 3D reconstruction had used different convolution neural
network (CNN) architectures to obtain a 3D voxel representation. Even though CNN works …