no code implementations • 2 Dec 2023 • Uy Dieu Tran, Minh Luu, Phong Nguyen, Janne Heikkila, Khoi Nguyen, Binh-Son Hua
Text-to-3D synthesis has recently emerged as a new approach to sampling 3D models by adopting pretrained text-to-image models as guiding visual priors.
no code implementations • 29 Sep 2022 • Snehal Bhayani, Viktor Larsson, Torsten Sattler, Janne Heikkila, Zuzana Kukelova
In this paper we study the problem of estimating the semi-generalized pose of a partially calibrated camera, i. e., the pose of a perspective camera with unknown focal length w. r. t.
no code implementations • 9 Aug 2022 • Phong Nguyen-Ha, Lam Huynh, Esa Rahtu, Jiri Matas, Janne Heikkila
Moreover, our method can leverage a denser set of reference images of a single scene to produce accurate novel views without relying on additional explicit representations and still maintains the high-speed rendering of the pre-trained model.
no code implementations • CVPR 2022 • Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkila, Tetsuya Sakai
First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-$K$ ranked list by treating the list as a set.
no code implementations • 3 Mar 2022 • Lam Huynh, Esa Rahtu, Jiri Matas, Janne Heikkila
We present LDP, a lightweight dense prediction neural architecture search (NAS) framework.
no code implementations • 27 Dec 2021 • Phong Nguyen-Ha, Nikolaos Sarafianos, Christoph Lassner, Janne Heikkila, Tony Tung
While prior work has shown impressive performance capture results in laboratory settings, it is non-trivial to achieve casual free-viewpoint human capture and rendering for unseen identities with high fidelity, especially for facial expressions, hands, and clothes.
no code implementations • 25 Aug 2021 • Lam Huynh, Phong Nguyen, Jiri Matas, Esa Rahtu, Janne Heikkila
This paper presents a novel neural architecture search method, called LiDNAS, for generating lightweight monocular depth estimation models.
no code implementations • 25 Aug 2021 • Lam Huynh, Matteo Pedone, Phong Nguyen, Jiri Matas, Esa Rahtu, Janne Heikkila
In addition, we introduce a normalized Hessian loss term invariant to scaling and shear along the depth direction, which is shown to substantially improve the accuracy.
1 code implementation • ICCV 2021 • Snehal Bhayani, Torsten Sattler, Daniel Barath, Patrik Beliansky, Janne Heikkila, Zuzana Kukelova
In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera.
no code implementations • ICCV 2021 • Lam Huynh, Phong Nguyen, Jiri Matas, Esa Rahtu, Janne Heikkila
In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance.
no code implementations • 29 Nov 2020 • Phong Nguyen, Animesh Karnewar, Lam Huynh, Esa Rahtu, Jiri Matas, Janne Heikkila
We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator network.
no code implementations • 9 Apr 2020 • Phong Nguyen-Ha, Lam Huynh, Esa Rahtu, Janne Heikkila
This paper addresses the problem of novel view synthesis by means of neural rendering, where we are interested in predicting the novel view at an arbitrary camera pose based on a given set of input images from other viewpoints.
2 code implementations • ECCV 2020 • Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, Janne Heikkila
Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations.
no code implementations • 10 Apr 2019 • Phong Nguyen-Ha, Lam Huynh, Esa Rahtu, Janne Heikkila
The problem of predicting a novel view of the scene using an arbitrary number of observations is a challenging problem for computers as well as for humans.
no code implementations • ICCV 2017 • Janne Heikkila
Finding a closed form solution to a system of polynomial equations is a common problem in computer vision as well as in many other areas of engineering and science.