1.2k citations · 1.6k across the 55 of their papers we have counts for
65 papers
GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training
Yejun Zhang, Xinjue Wang, Zihan Wang +2
Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its accuracy still lags far behind…
SplatGuide: Geometric Priors from 3D Gaussians for Pose-Free Novel View Synthesis
Yejun Zhang, Zihan Wang, Xu Ji +8
Generating photorealistic novel views from unposed images requires both 3D geometric understanding and the ability to synthesize unseen content. A natural strategy combines feed-fo…
VolHuMe: a High-Resolution Large Scale Dataset of Volumetric Human Meshes
Giulia Martinelli, Niccolò Bisagno, Nicola Garau +2
We introduce VolHuMe, a dataset of high-quality 4D human scans captured with a state-of-the-art volumetric studio using 64 RGB and 32 depth cameras. VolHuMe contains individual cap…
3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization
Ulas Gunes, Matias Turkulainen, Mikhail Silaev +2
We present the first evaluation of 3D Gaussian Splatting methods on real fisheye imagery with fields of view above 180\textdegree{}. Our study evaluates Fisheye-GS \cite{liao2024fi…
FIORD: A Fisheye Indoor-Outdoor Dataset with LIDAR Ground Truth for 3D Scene Reconstruction and Benchmarking
Ulas Gunes, Matias Turkulainen, Xuqian Ren +3
The development of large-scale 3D scene reconstruction and novel view synthesis methods mostly rely on datasets comprising perspective images with narrow fields of view (FoV). Whil…
AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones
Xuqian Ren, Matias Turkulainen, Jiepeng Wang +4
Geometric priors are often used to enhance 3D reconstruction. With many smartphones featuring low-resolution depth sensors and the prevalence of off-the-shelf monocular geometry es…
DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing
Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov +3
High-fidelity 3D reconstruction of common indoor scenes is crucial for VR and AR applications. 3D Gaussian splatting, a novel differentiable rendering technique, has achieved state…
Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion
Otto Seiskari, Jerry Ylilammi, Valtteri Kaatrasalo +5
High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with…
GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with 3D Gaussian Splatting
Dingding Cai, Janne Heikkilä, Esa Rahtu
This paper introduces GS-Pose, a unified framework for localizing and estimating the 6D pose of novel objects. GS-Pose begins with a set of posed RGB images of a previously unseen…
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis
Xuqian Ren, Wenjia Wang, Dingding Cai +3
Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and…
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
Mayu Otani, Riku Togashi, Yu Sawai +5
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images. How…
MSDA: Monocular Self-supervised Domain Adaptation for 6D Object Pose Estimation
Dingding Cai, Janne Heikkilä, Esa Rahtu
Acquiring labeled 6D poses from real images is an expensive and time-consuming task. Though massive amounts of synthetic RGB images are easy to obtain, the models trained on them s…
BS3D: Building-scale 3D Reconstruction from RGB-D Images
Janne Mustaniemi, Juho Kannala, Esa Rahtu +2
Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete groun…
SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
Dingding Cai, Janne Heikkilä, Esa Rahtu
This paper presents an efficient symmetry-agnostic and correspondence-free framework, referred to as SC6D, for 6D object pose estimation from a single monocular RGB image. SC6D req…
The Weighting Game: Evaluating Quality of Explainability Methods
Lassi Raatikainen, Esa Rahtu
The objective of this paper is to assess the quality of explanation heatmaps for image classification tasks. To assess the quality of explainability methods, we approach the task t…
Cascaded and Generalizable Neural Radiance Fields for Fast View Synthesis
Phong Nguyen-Ha, Lam Huynh, Esa Rahtu +2
We present CG-NeRF, a cascade and generalizable neural radiance fields method for view synthesis. Recent generalizing view synthesis methods can render high-quality novel views usi…
OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation
Dingding Cai, Janne Heikkilä, Esa Rahtu
This paper proposes a universal framework, called OVE6D, for model-based 6D object pose estimation from a single depth image and a target object mask. Our model is trained using pu…
Online panoptic 3D reconstruction as a Linear Assignment Problem
Leevi Raivio, Esa Rahtu
Real-time holistic scene understanding would allow machines to interpret their surrounding in a much more detailed manner than is currently possible. While panoptic image segmentat…
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval
Riku Togashi, Mayu Otani, Yuta Nakashima +3
Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applic…
Optimal Correction Cost for Object Detection Evaluation
Mayu Otani, Riku Togashi, Yuta Nakashima +3
Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms o…
Fast Neural Architecture Search for Lightweight Dense Prediction Networks
Lam Huynh, Esa Rahtu, Jiri Matas +1
We present LDP, a lightweight dense prediction neural architecture search (NAS) framework. Starting from a pre-defined generic backbone, LDP applies the novel Assisted Tabu Search…
Towards a Real-Time Facial Analysis System
Bishwo Adhikari, Xingyang Ni, Esa Rahtu +1
Facial analysis is an active research area in computer vision, with many practical applications. Most of the existing studies focus on addressing one specific task and maximizing i…
Image coding for machines: an end-to-end learned approach
Nam Le, Honglei Zhang, Francesco Cricri +2
Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for thos…
Learned Image Coding for Machines: A Content-Adaptive Approach
Nam Le, Honglei Zhang, Francesco Cricri +3
Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing category of Internet traffic is machine-to-machine communication. In particular, machine-to-ma…