Search Results for author: Janne Heikkila

Found 15 papers, 2 papers with code

DiverseDream: Diverse Text-to-3D Synthesis with Augmented Text Embedding

no code implementations2 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.

Text to 3D

Partially calibrated semi-generalized pose from hybrid point correspondences

no code implementations29 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.

Cascaded and Generalizable Neural Radiance Fields for Fast View Synthesis

no code implementations9 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.

Neural Rendering Novel View Synthesis

AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

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.

Moment Retrieval Retrieval

Free-Viewpoint RGB-D Human Performance Capture and Rendering

no code implementations27 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.

Neural Rendering Novel View Synthesis

Lightweight Monocular Depth with a Novel Neural Architecture Search Method

no code implementations25 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.

Monocular Depth Estimation Neural Architecture Search

Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss

no code implementations25 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.

Monocular Depth Estimation

Calibrated and Partially Calibrated Semi-Generalized Homographies

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.

Image-Based Localization

RGBD-Net: Predicting color and depth images for novel views synthesis

no code implementations29 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.

Novel View Synthesis regression

Sequential View Synthesis with Transformer

no code implementations9 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.

Neural Rendering Novel View Synthesis

Guiding Monocular Depth Estimation Using Depth-Attention Volume

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.

Monocular Depth Estimation

Predicting Novel Views Using Generative Adversarial Query Network

no code implementations10 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.

Novel View Synthesis

Using Sparse Elimination for Solving Minimal Problems in Computer Vision

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.

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