# Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation

arXiv: https://arxiv.org/abs/1811.12008 · HTML: https://mlsanity.com/paper/1811.12008/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation](https://mlsanity.com/paper/2309.13248/) (2023)
- [Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones](https://mlsanity.com/paper/2202.01421/) (2022)

## Abstract (arXiv)

The ability to perform semantic segmentation in real-time capable applications with limited hardware is of great importance. One such application is the interpretation of the visual bird's-eye view, which requires the semantic segmentation of the four omnidirectional camera images. In this paper, we present an efficient semantic segmentation that sets new standards in terms of runtime and hardware requirements. Our two main contributions are the decrease of the runtime by parallelizing the ArgMax layer and the reduction of hardware requirements by applying the channel pruning method to the ENet model.