arXiv Computer Vision By Daojie Peng, Bingtao Wang, Fulong Ma, Liang Zhang, Jun Ma

LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation

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LiteViLNet is a lightweight RGB‑geometry fusion network for road segmentation that uses a MobileNetV3 RGB encoder and a tiny depth‑wise‑separable geometry encoder. Its multi‑scale fusion module enhances modality‑specific features, performs cross‑modal interaction, and applies adaptive gating, while a depth‑wise large‑kernel bridge expands contextual support with minimal overhead. The U‑Net‑style decoder is trained with deep supervision, achieving state‑of‑the‑art performance on KITTI and ORFD benchmarks and running at up to 68.73 FPS on a Jetson Orin NX with TensorRT FP16.

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arXiv Computer Vision
Sep 7

Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation

The paper introduces FlexDepth, a family of self‑supervised monocular depth estimation models designed for robust driving perception. FlexDepth uses a two‑stage static‑dynamic decoupled training strategy and a Scale‑Driven Decoder that selects components based on scale size, enabling efficient feature fusion and high‑precision depth maps. Experiments on driving benchmarks show state‑of‑the‑art performance across arbitrary scales with minimal computational cost, with the smallest model (Flex‑Nano) achieving 37.6 FPS on mobile devices.

By Zhaowen Zhu, Li Zhang, Yujie Chen, Tian Zhang, Yingjie Wang, Mingxia Zhan