1 citations · 1 across the 9 of their papers we have counts for
9 papers
SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting
Kristof Overdulve, Lode Jorissen, Nick Michiels
Novel View Synthesis (NVS) methods, such as 3D Gaussian Splatting (3DGS), rely on the assumption of clean, multi-view consistent, posed input images. Real-world captures can violat…
Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection
Joren Michels, Lode Jorissen, Nick Michiels
The visual quality of AI-generated videos has improved drastically in recent years, making it increasingly difficult for humans to distinguish between real and synthetic media. In…
Practical High-Fidelity Novel-View Synthesis of Mounted Lepidoptera
Kristof Overdulve, Lode Jorissen, Nick Michiels
Mounted butterflies are among the most striking objects in natural history collections. However, their beauty is notoriously hard to digitize in 3D: they are small and fragile, wit…
CRISTAL: Real-time Camera Registration in Static LiDAR Scans using Neural Rendering
Joni Vanherck, Steven Moonen, Brent Zoomers +4
Accurate camera localization is crucial for robotics and Extended Reality (XR), enabling reliable navigation and alignment of virtual and real content. Existing visual methods ofte…
NVGS: Neural Visibility for Occlusion Culling in 3D Gaussian Splatting
Brent Zoomers, Florian Hahlbohm, Joni Vanherck +3
3D Gaussian Splatting can exploit frustum culling and level-of-detail strategies to accelerate rendering of scenes containing a large number of primitives. However, the semi-transp…
Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data Generation
Bram Vanherle, Brent Zoomers, Jeroen Put +2
Generating synthetic images is a useful method for cheaply obtaining labeled data for training computer vision models. However, obtaining accurate 3D models of relevant objects is…
Real-time Neural Rendering of LiDAR Point Clouds
Joni Vanherck, Brent Zoomers, Tom Mertens +2
Static LiDAR scanners produce accurate, dense, colored point clouds, but often contain obtrusive artifacts which makes them ill-suited for direct display. We propose an efficient m…
Genetic Learning for Designing Sim-to-Real Data Augmentations
Bram Vanherle, Nick Michiels, Frank Van Reeth
Data augmentations are useful in closing the sim-to-real domain gap when training on synthetic data. This is because they widen the training data distribution, thus encouraging the…
VATr++: Choose Your Words Wisely for Handwritten Text Generation
Bram Vanherle, Vittorio Pippi, Silvia Cascianelli +3
Styled Handwritten Text Generation (HTG) has received significant attention in recent years, propelled by the success of learning-based solutions employing GANs, Transformers, and,…