4 citations · 7 across the 15 of their papers we have counts for
20 papers
GenRec: Knowing Where to Reconstruct and Where to Generate
Ata Çelen, Jaewoo Jung, Federico Tombari +4
Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a unique correct value modulated on…
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…
OpenGaFF: Open-Vocabulary Gaussian Feature Field with Codebook Attention
Kunyi Li, Michael Niemeyer, Sen Wang +3
Understanding open-vocabulary 3D scenes with Gaussian-based representations remains challenging due to fragmented and spatially inconsistent semantic predictions across multi-view…
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…
Unified Semantic Transformer for 3D Scene Understanding
Sebastian Koch, Johanna Wald, Hidenobu Matsuki +3
Holistic 3D scene understanding involves capturing and parsing unstructured 3D environments. Due to the inherent complexity of the real world, existing models have predominantly be…
SegSplat: Feed-forward Gaussian Splatting and Open-Set Semantic Segmentation
Peter Siegel, Federico Tombari, Marc Pollefeys +1
We have introduced SegSplat, a novel framework designed to bridge the gap between rapid, feed-forward 3D reconstruction and rich, open-vocabulary semantic understanding. By constru…