# Evaluation of Multimodal Semantic Segmentation using RGB-D Data

Source: [Evaluation of Multimodal Semantic Segmentation using RGB-D Data](<https://www.sota2.com/research/paper/68fa699b1e209131cea2307a>)

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

Data updated: 2025-10-25T23:04:46.235Z

Authors: Jiesi Hu, Ganning Zhao, Suya You, C. C. Jay Kuo

Published: 2021-03-31T01:43:43.000Z

## Abstract

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environments. To achieve this, we are exploring and evaluating a range of related technology and solutions, including AI-driven multimodal scene perception, fusion, processing, and understanding. This work reports our efforts on the evaluation of a state-of-the-art approach for semantic segmentation with multiple RGB and depth sensing data. We employ four large datasets composed of diverse urban and terrain scenes and design various experimental methods and metrics. In addition, we also develop new strategies of multi-datasets learning to improve the detection and recognition of unseen objects. Extensive experiments, implementations, and results are reported in the paper.

## 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/2103.16758>)

[PDF](<https://arxiv.org/pdf/2103.16758.pdf>)