Presents the first large-scale infrared off-road dataset and a flow-free temporal model achieving state-of-the-art freespace detection performance with real-time inference.
Sne-roadseg: Incorporating surface normal information into semantic segmentation for accurate freespace detection,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
Proposes the first light field-LiDAR semantic segmentation dataset and the Mlpfseg network, which improves mIoU by 1.71 over image-only and 2.38 over point-cloud-only baselines via feature completion and depth perception modules.
citing papers explorer
-
Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark
Presents the first large-scale infrared off-road dataset and a flow-free temporal model achieving state-of-the-art freespace detection performance with real-time inference.
-
Geometry-Aware Cross Modal Alignment for Light Field-LiDAR Semantic Segmentation
Proposes the first light field-LiDAR semantic segmentation dataset and the Mlpfseg network, which improves mIoU by 1.71 over image-only and 2.38 over point-cloud-only baselines via feature completion and depth perception modules.