Neural Radiance Field in Autonomous Driving: A Survey
원문 초록
Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel view synthesis capabilities. With the rapid advancements in deep learning, a multitude of methods have emerged to explore the potential applications of NeRF in the domain of Autonomous Driving (AD). However, a conspicuous void is apparent within the current literature. To bridge this gap, this paper conducts a comprehensive survey of NeRF's applications in the context of AD. Our survey is structured to categorize NeRF's applications in Autonomous Driving (AD), specifically encompassing perception, 3D reconstruction, simultaneous localization and mapping (SLAM), and simulation. We delve into in-depth analysis and summarize the findings for each application category, and conclude by providing insights and discussions on future directions in this field. We hope this paper serves as a comprehensive reference for researchers in this domain. To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
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R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
Self-Supervised Multimodal NeRF for Autonomous Driving
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting
SPC to 3D: Novel View Synthesis from Binary SPC via I2I translation
Splat and Replace: 3D Reconstruction with Repetitive Elements
ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
토론
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