2 citations · 6 across the 8 of their papers we have counts for
8 papers
InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving
Xiaoyu Ye, Leheng Li, Xinyu Ji +8
Generating realistic, controllable, and temporally coherent urban environments is a critical yet unresolved challenge in the autonomous driving community. In this paper, we introdu…
SPOT-Occ: Sparse Prototype-guided Transformer for Camera-based 3D Occupancy Prediction
Suzeyu Chen, Leheng Li, Ying-Cong Chen
Achieving highly accurate and real-time 3D occupancy prediction from cameras is a critical requirement for the safe and practical deployment of autonomous vehicles. While this shif…
OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal Instruction
Leheng Li, Weichao Qiu, Xu Yan +6
We present OmniBooth, an image generation framework that enables spatial control with instance-level multi-modal customization. For all instances, the multimodal instruction can be…
SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIs
Leheng Li, Weichao Qiu, Yingjie Cai +4
The advancement of autonomous driving is increasingly reliant on high-quality annotated datasets, especially in the task of 3D occupancy prediction, where the occupancy labels requ…
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
Jing He, Haodong Li, Wei Yin +6
Leveraging the visual priors of pre-trained text-to-image diffusion models offers a promising solution to enhance zero-shot generalization in dense prediction tasks. However, exist…
Neural Radiance Field in Autonomous Driving: A Survey
Lei He, Leheng Li, Wenchao Sun +5
Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel…
Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF
Leheng Li, Qing Lian, Ying-Cong Chen
Deep neural networks (DNNs) have been proven extremely susceptible to adversarial examples, which raises special safety-critical concerns for DNN-based autonomous driving stacks (i…
Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
Leheng Li, Qing Lian, Luozhou Wang +2
This work explores the use of 3D generative models to synthesize training data for 3D vision tasks. The key requirements of the generative models are that the generated data should…