11 papers
PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
Haofei Xu, Rundi Wu, Philipp Henzler +7
State-of-the-art single-image 3D reconstruction methods often rely on complex hybrid architectures and loss functions, or compress geometry into latent spaces in order to leverage…
FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene Generation
Orest Kupyn, Goutam Bhat, Philipp Henzler +3
Generating explorable 3D scenes from a single image requires strong generative priors and accurate geometric representations suitable for downstream use. Current video diffusion mo…
Epipolar Geometry Improves Video Generation Models
Orest Kupyn, Théo Uscidda, Marta Tintore Gazulla +3
Video generation models have advanced significantly through the latent diffusion transformers trained with rectified flow techniques. Yet these models still struggle with geometric…
Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas
Felix Wimbauer, Fabian Manhardt, Michael Oechsle +4
The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and wor…
LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering
Jonas Kulhanek, Marie-Julie Rakotosaona, Fabian Manhardt +5
In this work, we present a novel level-of-detail (LOD) method for 3D Gaussian Splatting that enables real-time rendering of large-scale scenes on memory-constrained devices. Our ap…
Learning Neural Exposure Fields for View Synthesis
Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona +5
Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchma…