Lightweight road segmentation hits 97.23% MaxF, 68.73 FPS on Jetson
LiteViLNet self-reports 97.23% MaxF on KITTI Road with 14.04M parameters and 68.73 FPS under TensorRT FP16
ImportanceLocalEvidenceE2 unreplicated
A lightweight vision-LiDAR fusion network, LiteViLNet, self-reports 97.23±0.15% MaxF on the KITTI Road benchmark with only 14.04M parameters in the full model, and runs at 68.73 FPS under TensorRT FP16 inference on a Jetson Orin NX.
Previous lightweight road segmentation approaches often traded off between accuracy and embedded real-time performance, struggling to achieve both.
The network consists of MobileNetV3 plus a 0.12M-parameter geometric encoder; the authors self-report 97.23±0.15% MaxF on the KITTI Road benchmark, 22.18 FPS for the model alone under PyTorch FP16 inference on a Jetson Orin NX, and separately measured 68.73 FPS under TensorRT FP16. These are the authors' self-reported public benchmark results, with no independent verification yet.
The preprint LiteViLNet by Daojie Peng et al., v1 submitted on May 20, v3 revised on September 16, arXiv:2605.21007.