9 papers
Confidence-Based Mesh Extraction from 3D Gaussians
Lukas Radl, Felix Windisch, Andreas Kurz +3
Recently, 3D Gaussian Splatting (3DGS) greatly accelerated mesh extraction from posed images due to its explicit representation and fast software rasterization. While the addition…
Autoregressive Appearance Prediction for 3D Gaussian Avatars
Michael Steiner, Zhang Chen, Alexander Richard +3
A photorealistic and immersive human avatar experience demands capturing fine, person-specific details such as cloth and hair dynamics, subtle facial expressions, and characteristi…
A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory
Felix Windisch, Thomas Köhler, Lukas Radl +4
Gaussian Splatting has emerged as a high-performance technique for novel view synthesis, enabling real-time rendering and high-quality reconstruction of small scenes. However, scal…
SOF: Sorted Opacity Fields for Fast Unbounded Surface Reconstruction
Lukas Radl, Felix Windisch, Thomas Deixelberger +4
Recent advances in 3D Gaussian representations have significantly improved the quality and efficiency of image-based scene reconstruction. Their explicit nature facilitates real-ti…
VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality
Xuechang Tu, Lukas Radl, Michael Steiner +3
3D Gaussian Splatting (3DGS) has rapidly become a leading technique for novel-view synthesis, providing exceptional performance through efficient software-based GPU rasterization.…
AAA-Gaussians: Anti-Aliased and Artifact-Free 3D Gaussian Rendering
Michael Steiner, Thomas Köhler, Lukas Radl +3
Although 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction, it still faces challenges such as aliasing, projection artifacts, and view inconsistencies, primarily du…
StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering
Lukas Radl, Michael Steiner, Mathias Parger +3
Gaussian Splatting has emerged as a prominent model for constructing 3D representations from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting ren…
LAENeRF: Local Appearance Editing for Neural Radiance Fields
Lukas Radl, Michael Steiner, Andreas Kurz +1
Due to the omnipresence of Neural Radiance Fields (NeRFs), the interest towards editable implicit 3D representations has surged over the last years. However, editing implicit or hy…
Analyzing the Internals of Neural Radiance Fields
Lukas Radl, Andreas Kurz, Michael Steiner +1
Modern Neural Radiance Fields (NeRFs) learn a mapping from position to volumetric density leveraging proposal network samplers. In contrast to the coarse-to-fine sampling approach…