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

Source: [Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation](<https://www.sota2.com/research/paper/68fa651f1e209131cea0889a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:19:52.933Z

Authors: Timo S\\"amann, Karl Amende, Stefan Milz, Christian Witt, Martin Simon, Johannes Petzold

Published: 2018-11-29T08:21:18.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.12008>)

[PDF](<https://arxiv.org/pdf/1811.12008.pdf>)