Search Results for author: Christos Sakaridis

Found 34 papers, 24 papers with code

UniDepth: Universal Monocular Metric Depth Estimation

1 code implementation27 Mar 2024 Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc van Gool, Fisher Yu

However, the remarkable accuracy of recent MMDE methods is confined to their training domains.

 Ranked #1 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)

Monocular Depth Estimation

Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes

no code implementations27 Jan 2024 Diandian Guo, Deng-Ping Fan, Tongyu Lu, Christos Sakaridis, Luc van Gool

The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes.

Motion Estimation Segmentation +2

MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty

no code implementations23 Jan 2024 Tim Brödermann, David Bruggemann, Christos Sakaridis, Kevin Ta, Odysseas Liagouris, Jason Corkill, Luc van Gool

Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions.

Autonomous Vehicles Panoptic Segmentation

MuRF: Multi-Baseline Radiance Fields

1 code implementation7 Dec 2023 Haofei Xu, Anpei Chen, Yuedong Chen, Christos Sakaridis, Yulun Zhang, Marc Pollefeys, Andreas Geiger, Fisher Yu

We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large baselines, and different number of input views).

Zero-shot Generalization

Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding

1 code implementation NeurIPS 2023 Zhejun Zhang, Alexander Liniger, Christos Sakaridis, Fisher Yu, Luc van Gool

The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding traffic participants.

Autonomous Driving motion prediction

Three Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding

no code implementations8 Sep 2023 Ozan Unal, Christos Sakaridis, Suman Saha, Fisher Yu, Luc van Gool

A common formulation to tackle 3D visual grounding is grounding-by-detection, where localization is done via bounding boxes.

3D Instance Segmentation Object +3

Condition-Invariant Semantic Segmentation

1 code implementation27 May 2023 Christos Sakaridis, David Bruggemann, Fisher Yu, Luc van Gool

Motivated by these findings, we propose to leverage stylization in performing feature-level adaptation by aligning the internal network features extracted by the encoder of the network from the original and the stylized view of each input image with a novel feature invariance loss.

Segmentation Semantic Segmentation +1

Maskomaly:Zero-Shot Mask Anomaly Segmentation

no code implementations26 May 2023 Jan Ackermann, Christos Sakaridis, Fisher Yu

We present a simple and practical framework for anomaly segmentation called Maskomaly.

Informativeness Segmentation +1

Indiscernible Object Counting in Underwater Scenes

1 code implementation CVPR 2023 Guolei Sun, Zhaochong An, Yun Liu, Ce Liu, Christos Sakaridis, Deng-Ping Fan, Luc van Gool

We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings.

Benchmarking Object +2

Advances in Deep Concealed Scene Understanding

1 code implementation21 Apr 2023 Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, Luc van Gool

Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage.

Scene Understanding Semantic Segmentation

iDisc: Internal Discretization for Monocular Depth Estimation

2 code implementations CVPR 2023 Luigi Piccinelli, Christos Sakaridis, Fisher Yu

Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark.

Autonomous Driving Monocular Depth Estimation +3

CamDiff: Camouflage Image Augmentation via Diffusion Model

1 code implementation11 Apr 2023 Xue-Jing Luo, Shuo Wang, Zongwei Wu, Christos Sakaridis, Yun Cheng, Deng-Ping Fan, Luc van Gool

Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt.

Image Augmentation Image Classification +3

OVeNet: Offset Vector Network for Semantic Segmentation

1 code implementation25 Mar 2023 Stamatis Alexandropoulos, Christos Sakaridis, Petros Maragos

Motivated by this prior, we design a novel two-head network, named Offset Vector Network (OVeNet), which generates both standard semantic predictions and a dense 2D offset vector field indicating the offset from each pixel to the respective seed pixel, which is used to compute an alternative, seed-based semantic prediction.

Optical Character Recognition (OCR) Scene Understanding +1

Masked Vision-Language Transformer in Fashion

1 code implementation27 Oct 2022 Ge-Peng Ji, Mingcheng Zhuge, Dehong Gao, Deng-Ping Fan, Christos Sakaridis, Luc van Gool

We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation.

Image Reconstruction Retrieval

TT-NF: Tensor Train Neural Fields

1 code implementation30 Sep 2022 Anton Obukhov, Mikhail Usvyatsov, Christos Sakaridis, Konrad Schindler, Luc van Gool

Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations.

Denoising Low-rank compression

Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions

1 code implementation14 Jul 2022 David Bruggemann, Christos Sakaridis, Prune Truong, Luc van Gool

Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) for the semantic segmentation of such images.

Semantic Segmentation Unsupervised Domain Adaptation

L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras

1 code implementation3 Jul 2022 Kevin Ta, David Bruggemann, Tim Brödermann, Christos Sakaridis, Luc van Gool

As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research.

Autonomous Driving Camera Calibration

HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection

1 code implementation30 Jun 2022 Tim Broedermann, Christos Sakaridis, Dengxin Dai, Luc van Gool

Besides standard cameras, autonomous vehicles typically include multiple additional sensors, such as lidars and radars, which help acquire richer information for perceiving the content of the driving scene.

Autonomous Vehicles object-detection +3

LiDAR Snowfall Simulation for Robust 3D Object Detection

1 code implementation CVPR 2022 Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, Luc van Gool

Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds.

Autonomous Driving Object +3

Event-Based Fusion for Motion Deblurring with Cross-modal Attention

1 code implementation30 Nov 2021 Lei Sun, Christos Sakaridis, Jingyun Liang, Qi Jiang, Kailun Yang, Peng Sun, Yaozu Ye, Kaiwei Wang, Luc van Gool

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times.

Ranked #3 on Deblurring on GoPro (using extra training data)

Deblurring Image Deblurring +1

Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

1 code implementation ICCV 2021 Martin Hahner, Christos Sakaridis, Dengxin Dai, Luc van Gool

2) Through extensive experiments with several state-of-the-art detection approaches, we show that our fog simulation can be leveraged to significantly improve the performance for 3D object detection in the presence of fog.

3D Object Detection Object +3

ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding

no code implementations ICCV 2021 Christos Sakaridis, Dengxin Dai, Luc van Gool

To address this, we introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions.

Scene Understanding Segmentation +2

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

1 code implementation28 May 2020 Christos Sakaridis, Dengxin Dai, Luc van Gool

Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross-time-of-day correspondences between daytime images from a reference map and dark images to guide the label inference in the dark domains; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 201 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark for our novel evaluation.

Domain Adaptation Image Segmentation +2

Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

1 code implementation ICCV 2019 Christos Sakaridis, Dengxin Dai, Luc van Gool

Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night via labeled synthetic images and unlabeled real images, both for progressively darker times of day, which exploits cross-time-of-day correspondences for the real images to guide the inference of their labels; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, designed for adverse conditions and including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 151 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark to perform our novel evaluation.

Image Segmentation Segmentation +2

Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding

1 code implementation5 Jan 2019 Dengxin Dai, Christos Sakaridis, Simon Hecker, Luc van Gool

The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog).

Domain Adaptation Scene Understanding +2

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

no code implementations ECCV 2018 Christos Sakaridis, Dengxin Dai, Simon Hecker, Luc van Gool

In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog.

Scene Understanding Semantic Segmentation

Semantic Foggy Scene Understanding with Synthetic Data

no code implementations25 Aug 2017 Christos Sakaridis, Dengxin Dai, Luc van Gool

Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN).

Image Dehazing object-detection +3

Theoretical Analysis of Active Contours on Graphs

no code implementations24 Oct 2016 Christos Sakaridis, Kimon Drakopoulos, Petros Maragos

Active contour models based on partial differential equations have proved successful in image segmentation, yet the study of their geometric formulation on arbitrary geometric graphs is still at an early stage.

Image Segmentation Semantic Segmentation

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