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20172026
most citedEmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision

18 citations · 70 across the 54 of their papers we have counts for

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Showing 2024Show all

21 papers · 1 filter

cs.CV20241 cited

STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes

Jiawei Yang, Jiahui Huang, Yuxiao Chen +10

We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods often…

cs.CV2024

LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models

Ziqi Lu, Heng Yang, Danfei Xu +4

Emerging 3D geometric foundation models, such as DUSt3R, offer a promising approach for in-the-wild 3D vision tasks. However, due to the high-dimensional nature of the problem spac…

cs.LG2024

Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Zhejun Zhang, Peter Karkus, Maximilian Igl +4

Traffic simulation aims to learn a policy for traffic agents that, when unrolled in closed-loop, faithfully recovers the joint distribution of trajectories observed in the real wor…

cs.CV2024

Extrapolated Urban View Synthesis Benchmark

Xiangyu Han, Zhen Jia, Boyi Li +8

Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs). At their core is Novel View Synthesis (NVS), a crucial capabili…

cs.CV2024

Large Spatial Model: End-to-end Unposed Images to Semantic 3D

Zhiwen Fan, Jian Zhang, Wenyan Cong +10

Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into mu…

cs.RO2024

Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Zhiyu Huang, Xinshuo Weng, Maximilian Igl +5

Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the \tex…