6 citations · 8 across the 8 of their papers we have counts for
10 papers
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
NVIDIA, :, Aarti Basant +33
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving pol…
DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer
Yuxuan Zhang, Katarína Tóthová, Zian Wang +7
Simulation is essential to the development and evaluation of autonomous robots such as self-driving vehicles. Neural reconstruction is emerging as a promising solution as it enable…
ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models
Riccardo de Lutio, Tobias Fischer, Yen-Yu Chang +7
Per-scene optimization methods such as 3D Gaussian Splatting provide state-of-the-art novel view synthesis quality but extrapolate poorly to under-observed areas. Methods that leve…
SimULi: Real-Time LiDAR and Camera Simulation with Unscented Transforms
Haithem Turki, Qi Wu, Xin Kang +5
Rigorous testing of autonomous robots, such as self-driving vehicles, is essential to ensure their safety in real-world deployments. This requires building high-fidelity simulators…
Towards Learning to Complete Anything in Lidar
Ayca Takmaz, Cristiano Saltori, Neehar Peri +4
We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contem…
OmniRe: Omni Urban Scene Reconstruction
Ziyu Chen, Jiawei Yang, Jiahui Huang +9
We introduce OmniRe, a comprehensive system for efficiently creating high-fidelity digital twins of dynamic real-world scenes from on-device logs. Recent methods using neural field…
Learning Graph Regularisation for Guided Super-Resolution
Riccardo de Lutio, Alexander Becker, Stefano D'Aronco +3
We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. The learned graph potential…
Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs
Riccardo de Lutio, Yihang She, Stefano D'Aronco +4
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However,…
The Herbarium 2021 Half-Earth Challenge Dataset
Riccardo de Lutio, Damon Little, Barbara Ambrose +1
Herbarium sheets present a unique view of the world's botanical history, evolution, and diversity. This makes them an all-important data source for botanical research. With the inc…
Guided Super-Resolution as Pixel-to-Pixel Transformation
Riccardo de Lutio, Stefano D'Aronco, Jan Dirk Wegner +1
Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e.g., perspective dept…