19 citations · 44 across the 22 of their papers we have counts for
24 papers
IAE-VTG: Interaction-Aligned Action-Entity Video Temporal Grounding
Shiwen Zhao, Qi Zhang, Sezer Karaoglu +2
Video Temporal Grounding (VTG) localizes the video segment that matches a natural-language query. Many queries describe an action performed by a particular entity. Existing methods…
Unblur-SLAM: Dense Neural SLAM for Blurry Inputs
Qi Zhang, Denis Rozumny, Francesco Girlanda +4
We propose Unblur-SLAM, a novel RGB SLAM pipeline for sharp 3D reconstruction from blurred image inputs. In contrast to previous work, our approach is able to handle different type…
Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
Xiaoyan Xing, Xiao Zhang, Sezer Karaoglu +2
Image-to-image relighting requires representations that separate illumination from scene properties while preserving dense geometry, material, and photometric cues. We use this tas…
Grab-3D: Detecting AI-Generated Videos from 3D Geometric Temporal Consistency
Wenhan Chen, Sezer Karaoglu, Theo Gevers
Recent advances in diffusion-based generation techniques enable AI models to produce highly realistic videos, heightening the need for reliable detection mechanisms. However, exist…
InStyle: Instant Appearance Stylization of 3D Shapes
Melis Ocal, Xiaoyan Xing, Yue Li +3
3D stylization is central to game development, virtual reality, and digital arts, where the demand for diverse assets calls for scalable methods that support fast, high-fidelity ma…
Edge-Centric Relational Reasoning for 3D Scene Graph Prediction
Yanni Ma, Hao Liu, Yulan Guo +2
3D scene graph prediction aims to abstract complex 3D environments into structured graphs consisting of objects and their pairwise relationships. Existing approaches typically adop…
Monocular Visual Odometry without Calibration or Test-time Optimization
Vladimir Yugay, Vlardimir Yugay, Duy-Kien Nguyen +3
The most accurate monocular visual odometry systems require known camera intrinsics, refine their estimates with test-time optimization, and recover trajectories only up to an unkn…
Gaussian Mapping for Evolving Scenes
Vladimir Yugay, Thies Kersten, Luca Carlone +3
Mapping systems with novel view synthesis (NVS) capabilities, most notably 3D Gaussian Splatting (3DGS), are widely used in computer vision, as well as in various applications, inc…
LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relighting
Xiaoyan Xing, Konrad Groh, Sezer Karaoglu +2
We introduce LumiNet, a novel architecture that leverages generative models and latent intrinsic representations for effective lighting transfer. Given a source image and a target…
MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM
Vladimir Yugay, Theo Gevers, Martin R. Oswald
Simultaneous localization and mapping (SLAM) systems with novel view synthesis capabilities are widely used in computer vision, with applications in augmented reality, robotics, an…
FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training
Ruihong Yin, Vladimir Yugay, Yue Li +2
The field of novel view synthesis from images has seen rapid advancements with the introduction of Neural Radiance Fields (NeRF) and more recently with 3D Gaussian Splatting. Gauss…
RealDiff: Real-world 3D Shape Completion using Self-Supervised Diffusion Models
Başak Melis Öcal, Maxim Tatarchenko, Sezer Karaoglu +1
Point cloud completion aims to recover the complete 3D shape of an object from partial observations. While approaches relying on synthetic shape priors achieved promising results i…
Geometry-guided Feature Learning and Fusion for Indoor Scene Reconstruction
Ruihong Yin, Sezer Karaoglu, Theo Gevers
In addition to color and textural information, geometry provides important cues for 3D scene reconstruction. However, current reconstruction methods only include geometry at the fe…
Ray-Distance Volume Rendering for Neural Scene Reconstruction
Ruihong Yin, Yunlu Chen, Sezer Karaoglu +1
Existing methods in neural scene reconstruction utilize the Signed Distance Function (SDF) to model the density function. However, in indoor scenes, the density computed from the S…
SceneTeller: Language-to-3D Scene Generation
Başak Melis Öcal, Maxim Tatarchenko, Sezer Karaoglu +1
Designing high-quality indoor 3D scenes is important in many practical applications, such as room planning or game development. Conventionally, this has been a time-consuming proce…
Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory
Xiaoyan Xing, Vincent Tao Hu, Jan Hendrik Metzen +3
This paper introduces a novel approach to illumination manipulation in diffusion models, addressing the gap in conditional image generation with a focus on lighting conditions. We…
3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation
Weijie Wei, Osman Ülger, Fatemeh Karimi Nejadasl +2
Open-Vocabulary Segmentation (OVS) methods offer promising capabilities in detecting unseen object categories, but the category must be known and needs to be provided by a human, e…
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning
Weijie Wei, Fatemeh Karimi Nejadasl, Theo Gevers +1
The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning. Consequently, scholars have been actively investigating efficacious sel…
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
Vladimir Yugay, Yue Li, Theo Gevers +1
We present a dense simultaneous localization and mapping (SLAM) method that uses 3D Gaussians as a scene representation. Our approach enables interactive-time reconstruction and ph…
Relational Prior Knowledge Graphs for Detection and Instance Segmentation
Osman Ülger, Yu Wang, Ysbrand Galama +3
Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects. In this paper, we investigate the effectiven…
APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds
Weijie Wei, Martin R. Oswald, Fatemeh Karimi Nejadasl +1
In this paper, we focus on semantic segmentation method for point clouds of urban scenes. Our fundamental concept revolves around the collaborative utilization of diverse scene rep…
Invariant Descriptors for Intrinsic Reflectance Optimization
Anil S. Baslamisli, Theo Gevers
Intrinsic image decomposition aims to factorize an image into albedo (reflectance) and shading (illumination) sub-components. Being ill-posed and under-constrained, it is a very ch…
Physics-based Shading Reconstruction for Intrinsic Image Decomposition
Anil S. Baslamisli, Yang Liu, Sezer Karaoglu +1
We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading). We propose albedo and shading gradient descriptors which are de…
ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition
Anil S. Baslamisli, Partha Das, Hoang-An Le +2
In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother tha…