25 citations · 63 across the 15 of their papers we have counts for
12 papers · 1 filter
Compact Neural Appearance Models for Efficient Gaussian Splatting
Florian Hahlbohm, Jorge Condor, Linus Franke +2
Explicit primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearance using low-order spherical harmonics (SH). Although efficient to eva…
Immediate 3D Gaussian Splat Reconstruction of Unordered Input with Global Consistency
Andreas Meuleman, Linus Franke, Boris Zhestiankin +2
3D Gaussian Splatting (3DGS) has become the method of choice for reconstructing and real-time rendering of captured scenes. To capture a scene with good visual quality, continuous…
Faster-GS: Analyzing and Improving Gaussian Splatting Optimization
Florian Hahlbohm, Linus Franke, Martin Eisemann +1
Recent advances in 3D Gaussian Splatting (3DGS) have focused on accelerating optimization while preserving reconstruction quality. However, many proposed methods entangle implement…
SurfFill: Completion of LiDAR Point Clouds via Gaussian Surfel Splatting
Svenja Strobel, Matthias Innmann, Bernhard Egger +2
LiDAR-captured point clouds are often considered the gold standard in active 3D reconstruction. While their accuracy is exceptional in flat regions, the capturing is susceptible to…
Towards Integrating Multi-Spectral Imaging with Gaussian Splatting
Josef Grün, Lukas Meyer, Maximilian Weiherer +3
We present a study of how to integrate color (RGB) and multi-spectral imagery (red, green, red-edge, and near-infrared) into the 3D Gaussian Splatting (3DGS) framework, a state-of-…
VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Points
Linus Franke, Laura Fink, Marc Stamminger
Recent advances in novel view synthesis have demonstrated impressive results in fast photorealistic scene rendering through differentiable point rendering, either via Gaussian Spla…
Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering
Laura Fink, Linus Franke, Bernhard Egger +2
Accurate depth estimation is at the core of many applications in computer graphics, vision, and robotics. Current state-of-the-art monocular depth estimators, trained on extensive…
INPC: Implicit Neural Point Clouds for Radiance Field Rendering
Florian Hahlbohm, Linus Franke, Moritz Kappel +4
We introduce a new approach for reconstruction and novel view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based m…
TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Rendering
Linus Franke, Darius Rückert, Laura Fink +1
Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. Ho…
LiveNVS: Neural View Synthesis on Live RGB-D Streams
Laura Fink, Darius Rückert, Linus Franke +2
Existing real-time RGB-D reconstruction approaches, like Kinect Fusion, lack real-time photo-realistic visualization. This is due to noisy, oversmoothed or incomplete geometry and…
VET: Visual Error Tomography for Point Cloud Completion and High-Quality Neural Rendering
Linus Franke, Darius Rückert, Laura Fink +2
In the last few years, deep neural networks opened the doors for big advances in novel view synthesis. Many of these approaches are based on a (coarse) proxy geometry obtained by s…
ADOP: Approximate Differentiable One-Pixel Point Rendering
Darius Rückert, Linus Franke, Marc Stamminger
In this paper we present ADOP, a novel point-based, differentiable neural rendering pipeline. Like other neural renderers, our system takes as input calibrated camera images and a…