Search Results for author: Janne Heikkila

Found 12 papers, 2 papers with code

AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

no code implementations30 Mar 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

Human View Synthesis using a Single Sparse RGB-D Input

no code implementations27 Dec 2021 Phong Nguyen, Nikolaos Sarafianos, Christoph Lassner, Janne Heikkila, Tony Tung

We show our method generates high-quality novel views of synthetic and real human actors given a single sparse RGB-D input.

Neural Rendering Novel View Synthesis

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

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

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

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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