29 citations · 84 across the 27 of their papers we have counts for
7 papers · 1 filter
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…
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…