192 citations · 194 across the 6 of their papers we have counts for
6 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…
Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation
NVIDIA, :, Jiahui Huang +14
3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent yea…
TokenGS: Decoupling 3D Gaussian Prediction from Pixels with Learnable Tokens
Jiawei Ren, Michal Jan Tyszkiewicz, Jiahui Huang +1
In this work, we revisit several key design choices of modern Transformer-based approaches for feed-forward 3D Gaussian Splatting (3DGS) prediction. We argue that the common practi…
GECCO: Geometrically-Conditioned Point Diffusion Models
Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls
Diffusion models generating images conditionally on text, such as Dall-E 2 and Stable Diffusion, have recently made a splash far beyond the computer vision community. Here, we tack…
RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers
Michał J. Tyszkiewicz, Kevis-Kokitsi Maninis, Stefan Popov +1
We propose a transformer-based neural network architecture for multi-object 3D reconstruction from RGB videos. It relies on two alternative ways to represent its knowledge: as a gl…
DISK: Learning local features with policy gradient
Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls
Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIS…