19 citations · 22 across the 5 of their papers we have counts for
5 papers
Rethinking Image Super-Resolution from Training Data Perspectives
Go Ohtani, Ryu Tadokoro, Ryosuke Yamada +7
In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on com…
Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs
Brandon Smart, Chuanxia Zheng, Iro Laina +1
In this paper, we introduce Splatt3R, a pose-free, feed-forward method for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs. Given uncalibrated natural imag…
Scaling Backwards: Minimal Synthetic Pre-training?
Ryo Nakamura, Ryu Tadokoro, Ryosuke Yamada +6
Pre-training and transfer learning are an important building block of current computer vision systems. While pre-training is usually performed on large real-world image datasets, i…
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht +4
Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somew…
RealFusion: 360° Reconstruction of Any Object from a Single Image
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina +1
We consider the problem of reconstructing a full 360° photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find…