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arXiv · 2506.19615

Self-Supervised Multimodal NeRF for Autonomous Driving

Gaurav Sharma·Ravi Kothari·Josef Schmid
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Abstract

In this paper, we propose a Neural Radiance Fields (NeRF) based framework, referred to as Novel View Synthesis Framework (NVSF). It jointly learns the implicit neural representation of space and time-varying scene for both LiDAR and Camera. We test this on a real-world autonomous driving scenario containing both static and dynamic scenes. Compared to existing multimodal dynamic NeRFs, our framework is self-supervised, thus eliminating the need for 3D labels. For efficient training and faster convergence, we introduce heuristic-based image pixel sampling to focus on pixels with rich information. To preserve the local features of LiDAR points, a Double Gradient based mask is employed. Extensive experiments on the KITTI-360 dataset show that, compared to the baseline models, our framework has reported best performance on both LiDAR and Camera domain. Code of the model is available at https://github.com/gaurav00700/Selfsupervised-NVSF

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Gaurav Sharma, Ravi Kothari, Josef Schmid. 2025-06-24. Self-Supervised Multimodal NeRF for Autonomous Driving. https://arxiv.org/abs/2506.19615

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