Search Results for author: Peter Eisert

Found 54 papers, 9 papers with code

EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds

no code implementations19 Mar 2025 Yuanchao Yue, Hui Yuan, Qinglong Miao, Xiaolong Mao, Raouf Hamzaoui, Peter Eisert

We retain crucial information from the original data by extracting edge points and pixels, enhancing registration accuracy while maintaining computational efficiency.

Autonomous Driving Computational Efficiency +1

SPNeRF: Open Vocabulary 3D Neural Scene Segmentation with Superpoints

no code implementations19 Mar 2025 Weiwen Hu, Niccolò Parodi, Marcus Zepp, Ingo Feldmann, Oliver Schreer, Peter Eisert

Open-vocabulary segmentation, powered by large visual-language models like CLIP, has expanded 2D segmentation capabilities beyond fixed classes predefined by the dataset, enabling zero-shot understanding across diverse scenes.

NeRF Scene Segmentation +1

Improving Adaptive Density Control for 3D Gaussian Splatting

no code implementations18 Mar 2025 Glenn Grubert, Florian Barthel, Anna Hilsmann, Peter Eisert

Following the adaptive density control (ADC) mechanism of 3D Gaussian Splatting, new Gaussians in under-reconstructed regions are created, while Gaussians that do not contribute to the rendering quality are pruned.

3DGS Novel View Synthesis

Improving Geometric Consistency for 360-Degree Neural Radiance Fields in Indoor Scenarios

no code implementations17 Mar 2025 Iryna Repinetska, Anna Hilsmann, Peter Eisert

Photo-realistic rendering and novel view synthesis play a crucial role in human-computer interaction tasks, from gaming to path planning.

Depth Estimation NeRF +1

Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction

no code implementations5 Mar 2025 Mariusz Trzeciakiewicz, Aleixo Cambeiro Barreiro, Niklas Gard, Anna Hilsmann, Peter Eisert

This paper presents a method for image-based automated drywall analysis enabling construction progress and quality assessment through on-site camera systems.

Data Augmentation Instance Segmentation +2

Creating Sorted Grid Layouts with Gradient-based Optimization

no code implementations4 Mar 2025 Kai Uwe Barthel, Florian Tim Barthel, Peter Eisert, Nico Hezel, Konstantin Schall

Visually sorted grid layouts provide an efficient method for organizing high-dimensional vectors in two-dimensional space by aligning spatial proximity with similarity relationships.

Automatic Tissue Differentiation in Parotidectomy using Hyperspectral Imaging

no code implementations6 Dec 2024 Eric L. Wisotzky, Alexander Schill, Anna Hilsmann, Peter Eisert, Michael Knoke

For the analysis, 27 images with annotations of glandular tissue, nerve, muscle, skin and vein in 18 patients undergoing parotidectomy are included.

Multi-Resolution Generative Modeling of Human Motion from Limited Data

no code implementations25 Nov 2024 David Eduardo Moreno-Villamarín, Anna Hilsmann, Peter Eisert

We present a generative model that learns to synthesize human motion from limited training sequences.

Diversity

Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction

no code implementations10 Nov 2024 Decai Chen, Brianne Oberson, Ingo Feldmann, Oliver Schreer, Anna Hilsmann, Peter Eisert

Our method achieves superior accuracy and temporal coherence in dynamic surface reconstruction, delivering high-fidelity space-time novel view synthesis, even in complex and challenging scenes.

Dynamic Reconstruction Novel View Synthesis +1

Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization

no code implementations7 Oct 2024 Rohan Reddy Mekala, Frederik Pahde, Simon Baur, Sneha Chandrashekar, Madeline Diep, Markus Wenzel, Eric L. Wisotzky, Galip Ümit Yolcu, Sebastian Lapuschkin, Jackie Ma, Peter Eisert, Mikael Lindvall, Adam Porter, Wojciech Samek

In the realm of dermatological diagnoses, where the analysis of dermatoscopic and microscopic skin lesion images is pivotal for the accurate and early detection of various medical conditions, the costs associated with creating diverse and high-quality annotated datasets have hampered the accuracy and generalizability of machine learning models.

Form Generative Adversarial Network +2

3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods

no code implementations17 Jun 2024 Milena T. Bagdasarian, Paul Knoll, Yi-Hsin Li, Florian Barthel, Anna Hilsmann, Peter Eisert, Wieland Morgenstern

Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands.

3DGS SSIM +1

Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks

no code implementations16 Apr 2024 Florian Barthel, Arian Beckmann, Wieland Morgenstern, Anna Hilsmann, Peter Eisert

By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting attributes, we can integrate the representational diversity and quality of 3D GANs into the ecosystem of 3D Gaussian Splatting for the first time.

3DGS Decoder +2

SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments

1 code implementation16 Apr 2024 Niklas Gard, Anna Hilsmann, Peter Eisert

In this paper, we present SPVLoc, a global indoor localization method that accurately determines the six-dimensional (6D) camera pose of a query image and requires minimal scene-specific prior knowledge and no scene-specific training.

6D Pose Estimation Camera Localization +2

Video-Driven Animation of Neural Head Avatars

no code implementations7 Mar 2024 Wolfgang Paier, Paul Hinzer, Anna Hilsmann, Peter Eisert

We present a new approach for video-driven animation of high-quality neural 3D head models, addressing the challenge of person-independent animation from video input.

Compact 3D Scene Representation via Self-Organizing Gaussian Grids

2 code implementations19 Dec 2023 Wieland Morgenstern, Florian Barthel, Anna Hilsmann, Peter Eisert

In this paper, we introduce a compact scene representation organizing the parameters of 3D Gaussian Splatting (3DGS) into a 2D grid with local homogeneity, ensuring a drastic reduction in storage requirements without compromising visual quality during rendering.

3DGS +5

Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction

no code implementations15 Dec 2023 Eric L. Wisotzky, Jost Triller, Anna Hilsmann, Peter Eisert

To address these drawbacks, we present a novel approach combining two calibrated multispectral real-time capable snapshot cameras, covering different spectral ranges, into a stereo-system.

3D Reconstruction

Towards Better Morphed Face Images without Ghosting Artifacts

no code implementations13 Dec 2023 Clemens Seibold, Anna Hilsmann, Peter Eisert

Furthermore, we show that our approach does not impair the biometric quality, which is essential for high quality morphs.

MORPH Style Transfer

Multi-view Inversion for 3D-aware Generative Adversarial Networks

1 code implementation8 Dec 2023 Florian Barthel, Anna Hilsmann, Peter Eisert

Current 3D GAN inversion methods for human heads typically use only one single frontal image to reconstruct the whole 3D head model.

NeRF

Multi-task Planar Reconstruction with Feature Warping Guidance

1 code implementation25 Nov 2023 Luan Wei, Anna Hilsmann, Peter Eisert

We introduce SOLOPlanes, a real-time planar reconstruction model based on a modified instance segmentation architecture which simultaneously predicts semantics for each plane instance, along with plane parameters and piece-wise plane instance masks.

3D Reconstruction Instance Segmentation +4

Animating NeRFs from Texture Space: A Framework for Pose-Dependent Rendering of Human Performances

no code implementations6 Nov 2023 Paul Knoll, Wieland Morgenstern, Anna Hilsmann, Peter Eisert

The extension to a controllable synthesis of dynamic human performances poses an exciting research question.

NeRF

Human-Centered Evaluation of XAI Methods

no code implementations11 Oct 2023 Karam Dawoud, Wojciech Samek, Peter Eisert, Sebastian Lapuschkin, Sebastian Bosse

In the ever-evolving field of Artificial Intelligence, a critical challenge has been to decipher the decision-making processes within the so-called "black boxes" in deep learning.

Decision Making Image Classification

BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation

1 code implementation31 Aug 2023 Johannes Künzel, Anna Hilsmann, Peter Eisert

We introduce BTSeg (Barlow Twins regularized Segmentation), an innovative, semi-supervised training approach enhancing semantic segmentation models in order to effectively tackle adverse weather conditions without requiring additional labeled training data.

Autonomous Driving Domain Adaptation +3

A differentiable Gaussian Prototype Layer for explainable Segmentation

no code implementations25 Jun 2023 Michael Gerstenberger, Steffen Maaß, Peter Eisert, Sebastian Bosse

We introduce a Gaussian Prototype Layer for gradient-based prototype learning and demonstrate two novel network architectures for explainable segmentation one of which relies on region proposals.

Superpixels

Unsupervised Learning of Style-Aware Facial Animation from Real Acting Performances

no code implementations16 Jun 2023 Wolfgang Paier, Anna Hilsmann, Peter Eisert

This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering.

Neural Rendering

Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans

no code implementations2 Jun 2023 Aleixo Cambeiro Barreiro, Mariusz Trzeciakiewicz, Anna Hilsmann, Peter Eisert

Digitalization of existing buildings and the creation of 3D BIM models for them has become crucial for many tasks.

Towards L-System Captioning for Tree Reconstruction

no code implementations10 May 2023 Jannes S. Magnusson, Anna Hilsmann, Peter Eisert

This work proposes a novel concept for tree and plant reconstruction by directly inferring a Lindenmayer-System (L-System) word representation from image data in an image captioning approach.

Image Captioning

Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes

no code implementations9 May 2023 Arian Beckmann, Anna Hilsmann, Peter Eisert

Due to the rising threat of deepfakes to security and privacy, it is most important to develop robust and reliable detectors.

DeepFake Detection Face Swapping +1

Dynamic Multi-View Scene Reconstruction Using Neural Implicit Surface

no code implementations28 Feb 2023 Decai Chen, Haofei Lu, Ingo Feldmann, Oliver Schreer, Peter Eisert

Recent works represent the dynamic scene with neural radiance fields for photorealistic view synthesis, while their surface geometry is under-constrained and noisy.

Novel View Synthesis Surface Reconstruction

Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning

no code implementations21 Nov 2022 Eric L. Wisotzky, Charul Daudkhane, Anna Hilsmann, Peter Eisert

The dataset is a combination of real captured scenes with images from publicly available data adapted to the 4x4 mosaic pattern.

Deep Learning Demosaicking

CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation

1 code implementation11 Oct 2022 Niklas Gard, Anna Hilsmann, Peter Eisert

Applications in the field of augmented reality or robotics often require joint localisation and 6D pose estimation of multiple objects.

6D Pose Estimation 6D Pose Estimation using RGB +2

Perfusion assessment via local remote photoplethysmography (rPPG)

no code implementations29 Aug 2022 Benjamin Kossack, Eric Wisotzky, Peter Eisert, Sebastian P. Schraven, Brigitta Globke, Anna Hilsmann

From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue.

But that's not why: Inference adjustment by interactive prototype revision

no code implementations18 Mar 2022 Michael Gerstenberger, Sebastian Lapuschkin, Peter Eisert, Sebastian Bosse

It shows that even correct classifications can rely on unreasonable prototypes that result from confounding variables in a dataset.

BIG-bench Machine Learning Decision Making

Accurate Human Body Reconstruction for Volumetric Video

no code implementations26 Feb 2022 Decai Chen, Markus Worchel, Ingo Feldmann, Oliver Schreer, Peter Eisert

In this work, we enhance a professional end-to-end volumetric video production pipeline to achieve high-fidelity human body reconstruction using only passive cameras.

Stereo Matching Video Reconstruction

Imposing Temporal Consistency on Deep Monocular Body Shape and Pose Estimation

no code implementations7 Feb 2022 Alexandra Zimmer, Anna Hilsmann, Wieland Morgenstern, Peter Eisert

In detail, we derive parameters of a sequence of body models, representing shape and motion of a person, including jaw poses, facial expressions, and finger poses.

Pose Estimation

From Explanations to Segmentation: Using Explainable AI for Image Segmentation

no code implementations1 Feb 2022 Clemens Seibold, Johannes Künzel, Anna Hilsmann, Peter Eisert

The new era of image segmentation leveraging the power of Deep Neural Nets (DNNs) comes with a price tag: to train a neural network for pixel-wise segmentation, a large amount of training samples has to be manually labeled on pixel-precision.

Explainable Artificial Intelligence (XAI) Image Segmentation +3

Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects

no code implementations31 Jan 2022 Niklas Gard, Anna Hilsmann, Peter Eisert

In this paper, we present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects.

Object Pose Estimation

Automated Damage Inspection of Power Transmission Towers from UAV Images

no code implementations30 Nov 2021 Aleixo Cambeiro Barreiro, Clemens Seibold, Anna Hilsmann, Peter Eisert

Recently, the use of drones or helicopters for remote recording is increasing in the industry, sparing the technicians this perilous task.

Zero in on Shape: A Generic 2D-3D Instance Similarity Metric learned from Synthetic Data

no code implementations9 Aug 2021 Maciej Janik, Niklas Gard, Anna Hilsmann, Peter Eisert

We present a network architecture which compares RGB images and untextured 3D models by the similarity of the represented shape.

Retrieval

Focused LRP: Explainable AI for Face Morphing Attack Detection

no code implementations26 Mar 2021 Clemens Seibold, Anna Hilsmann, Peter Eisert

This evaluation framework is based on removing detected artifacts and analyzing the influence of these changes on the decision of the DNN.

Decision Making Face Morphing Attack Detection

Neural Face Models for Example-Based Visual Speech Synthesis

no code implementations22 Sep 2020 Wolfgang Paier, Anna Hilsmann, Peter Eisert

We solve these problems by combining the realism and simplicity of example-based animations with the advantages of neural face models.

Face Model Speech Synthesis

Going beyond Free Viewpoint: Creating Animatable Volumetric Video of Human Performances

no code implementations2 Sep 2020 Anna Hilsmann, Philipp Fechteler, Wieland Morgenstern, Wolfgang Paier, Ingo Feldmann, Oliver Schreer, Peter Eisert

Going beyond the application of free-viewpoint volumetric video, we allow re-animation and alteration of an actor's performance through (i) the enrichment of the captured data with semantics and animation properties and (ii) applying hybrid geometry- and video-based animation methods that allow a direct animation of the high-quality data itself instead of creating an animatable model that resembles the captured data.

Style Your Face Morph and Improve Your Face Morphing Attack Detector

no code implementations23 Apr 2020 Clemens Seibold, Anna Hilsmann, Peter Eisert

A morphed face image is a synthetically created image that looks so similar to the faces of two subjects that both can use it for verification against a biometric verification system.

Face Morphing Attack Detection MORPH +1

Automatic Analysis of Sewer Pipes Based on Unrolled Monocular Fisheye Images

no code implementations11 Dec 2019 Johannes Künzel, Thomas Werner, Ronja Möller, Peter Eisert, Jan Waschnewski, Ralf Hilpert

The task of detecting and classifying damages in sewer pipes offers an important application area for computer vision algorithms.

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