most citedGS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting

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cs.CV2026

DriveWeaver: Point-Conditioned Video Inpainting for Controllable Vehicle Insertion in Autonomous Driving Simulation

Junzhe Jiang, Zipei Ma, Zijie Pan +1

A pivotal step in autonomous driving simulation involves inserting foreground vehicles with predefined trajectories into simulated scenes. This process enhances scene diversity and…

cs.CV2026

3D Consistency Optimization for Self-Supervised Monocular Video Depth Estimation

Yuanye Liu, Ke Zhang, Junzhe Jiang +3

Reliable monocular video depth estimation is crucial for downstream 3D reasoning and embodied AI in endoscopic navigation. However, existing self-supervised approaches typically tr…

cs.CV2025

BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting

Zipei Ma, Junzhe Jiang, Yurui Chen +1

The realistic reconstruction of street scenes is critical for developing real-world simulators in autonomous driving. Most existing methods rely on object pose annotations, using t…

cs.CV2025

RealEngine: Simulating Autonomous Driving in Realistic Context

Junzhe Jiang, Nan Song, Jingyu Li +2

Driving simulation plays a crucial role in developing reliable driving agents by providing controlled, evaluative environments. To enable meaningful assessments, a high-quality dri…

cs.CV20251 cited

GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting

Junzhe Jiang, Chun Gu, Yurui Chen +1

LiDAR novel view synthesis (NVS) has emerged as a novel task within LiDAR simulation, offering valuable simulated point cloud data from novel viewpoints to aid in autonomous drivin…

cs.CV2023

Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering

Yurui Chen, Chun Gu, Junzhe Jiang +2

Modeling dynamic, large-scale urban scenes is challenging due to their highly intricate geometric structures and unconstrained dynamics in both space and time. Prior methods often…