25 citations · 42 across the 16 of their papers we have counts for
11 papers · 1 filter
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-…
FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields
Lukas Meyer, Andrei-Timotei Ardelean, Tim Weyrich +1
We introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orch…
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…
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
Lukas Meyer, Andreas Gilson, Ute Schmid +1
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes…
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…
PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DoF Object Pose Dataset Generation
Lukas Meyer, Floris Erich, Yusuke Yoshiyasu +3
We introduce Physically Enhanced Gaussian Splatting Simulation System (PEGASUS) for 6DOF object pose dataset generation, a versatile dataset generator based on 3D Gaussian Splattin…
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…