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Found 5 papers, 1 papers with code
DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation
To address this, we propose DiST-4D, the first disentangled spatiotemporal diffusion framework for 4D driving scene generation, which leverages metric depth as the core geometric representation.
MuDG: Taming Multi-modal Diffusion with Gaussian Splatting for Urban Scene Reconstruction
Recent breakthroughs in radiance fields have significantly advanced 3D scene reconstruction and novel view synthesis (NVS) in autonomous driving.
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction.
UniScene: Unified Occupancy-centric Driving Scene Generation
UniScene employs a progressive generation process that decomposes the complex task of scene generation into two hierarchical steps: (a) first generating semantic occupancy from a customized scene layout as a meta scene representation rich in both semantic and geometric information, and then (b) conditioned on occupancy, generating video and LiDAR data, respectively, with two novel transfer strategies of Gaussian-based Joint Rendering and Prior-guided Sparse Modeling.
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system.
