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From the 1 of 8 linked papers with an AI index.

collaborators

8 papers

cs.GR2026

Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

NVIDIA, :, Jiahui Huang +14

The paper introduces Instant NuRec, a feed‑forward neural model that converts a short multi‑camera driving log into a fully simulatable 3D Gaussian Splatting scene in about 1.5 sec…

cs.CV2026

Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation

Tianshi Cao, Jiawei Ren, Yuxuan Zhang +12

Closed-loop simulation is a core component of autonomous vehicle (AV) development, enabling scalable testing, training, and safety validation before real-world deployment. Neural s…

cs.CV2025

RadarGen: Automotive Radar Point Cloud Generation from Cameras

Tomer Borreda, Fangqiang Ding, Sanja Fidler +2

We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-latent diffusion to…

cs.CV2025

fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence

Francis Williams, Jiahui Huang, Jonathan Swartz +9

We present fVDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. fVDB provides a complete set of differentiable primitives to build deep learning architec…

cs.CV2025

Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos

Hanxue Liang, Jiawei Ren, Ashkan Mirzaei +8

Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle wi…

cs.CV2025

InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models

Yifan Lu, Xuanchi Ren, Jiawei Yang +8

We present InfiniCube, a scalable method for generating unbounded dynamic 3D driving scenes with high fidelity and controllability. Previous methods for scene generation either suf…