2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes
Partha Das, Sezer Karaoglu, Arjan Gijsenij +1
Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints…