PointForward uses sparse world-space 3D queries and scene graphs to deliver consistent single-pass reconstruction of dynamic driving scenes via point-aligned representations.
Street gaussians: Modeling dynamic urban scenes with gaussian splatting
5 Pith papers cite this work. Polarity classification is still indexing.
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MDrive benchmark shows multi-agent cooperative driving systems generally outperform single-agent ones in closed-loop settings but perception sharing does not always improve planning and negotiation can harm performance in complex traffic.
ConFixGS repairs feedforward 3D Gaussian Splatting with confidence-aware diffusion priors, delivering up to 3.68 dB PSNR gains and halved FID scores on Waymo, nuScenes, and KITTI novel view synthesis tasks.
DUST decouples pose trajectories per camera source while sharing canonical Gaussians per agent to remove cross-source gradient conflicts and ghosting caused by temporal asynchrony in 4D cooperative driving scenes.
Introduces Orthogonal Projected Gradient (OPG) and a smoothness-based temporal regularization to restore spatial identifiability and ensure physically consistent 4D scene reconstruction for closed-loop autonomous driving simulation.
citing papers explorer
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PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations
PointForward uses sparse world-space 3D queries and scene graphs to deliver consistent single-pass reconstruction of dynamic driving scenes via point-aligned representations.
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MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
MDrive benchmark shows multi-agent cooperative driving systems generally outperform single-agent ones in closed-loop settings but perception sharing does not always improve planning and negotiation can harm performance in complex traffic.
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ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
ConFixGS repairs feedforward 3D Gaussian Splatting with confidence-aware diffusion priors, delivering up to 3.68 dB PSNR gains and halved FID scores on Waymo, nuScenes, and KITTI novel view synthesis tasks.
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One World, Dual Timeline: Decoupled Spatio-Temporal Gaussian Scene Graph for 4D Cooperative Driving Reconstruction
DUST decouples pose trajectories per camera source while sharing canonical Gaussians per agent to remove cross-source gradient conflicts and ghosting caused by temporal asynchrony in 4D cooperative driving scenes.
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Towards Physically Consistent 4D Scene Reconstruction for Closed-loop Autonomous Driving Simulation
Introduces Orthogonal Projected Gradient (OPG) and a smoothness-based temporal regularization to restore spatial identifiability and ensure physically consistent 4D scene reconstruction for closed-loop autonomous driving simulation.