mlsanityData to 14 Oct 2026

LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction

arXiv 2302.06414 ↗First posted 10 Feb 2023cs.RO cs.AI cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Semantic grids can be useful representations of the scene around an autonomous system. By having information about the layout of the space around itself, a robot can leverage this type of representation for crucial tasks such as navigation or tracking. By fusing information from multiple sensors, robustness can be increased and the computational load for the task can be lowered, achieving real time performance. Our multi-scale LiDAR-Aided Perspective Transform network uses information available in point clouds to guide the projection of image features to a top-view representation, resulting in a relative improvement in the state of the art for semantic grid generation for human (+8.67%) and movable object (+49.07%) classes in the nuScenes dataset, as well as achieving results close to the state of the art for the vehicle, drivable area and walkway classes, while performing inference at 25 FPS.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 39 references that are in the map. First posted on arXiv 10 Feb 2023.

Builds on

24 of 39 references.

References · 24

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12 more
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  12. 2021PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Datacited by 16

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Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Drivingcited by 26
  2. 2024BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Drivingcited by 0