4 papers · 1 filter
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…