Enabling Multimodal Understanding: Lidar Data Meets VQA
- DOI
- 10.2991/978-94-6463-784-7_11How to use a DOI?
- Keywords
- LiDAR; Visual Question Answering (VQA); VQA Applications; Multimodality; Computer Vision; Natural Language Processing
- Abstract
This chapter explores the integration of Light Detection and Ranging (LiDAR) data with multimodal systems such as Visual Question Answering (VQA) to enable robust contextual understanding. The chapter begins with an overview of VQA, including its architecture, various modeling approaches, and practical applications. The chapter then introduces LiDAR data, particularly point clouds, and highlights how this 3D information enhances traditional visual input in machine learning tasks. Special attention is given to recent efforts in combining point cloud data with VQA, examining relevant datasets, deep learning models, and fusion techniques. The chapter concludes by outlining current limitations and future directions for advancing LiDAR-based multimodal understanding in VQA.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Muhammad Zeeshan Khan AU - Anuroop Gaddam AU - Dhananjay Thiruvady AU - N. K. Suryadevara PY - 2025 DA - 2025/07/28 TI - Enabling Multimodal Understanding: Lidar Data Meets VQA BT - Proceedings of the IoT AND LiDAR Technologies in Healthcare Workshop (ILTH 2024) PB - Atlantis Press SP - 110 EP - 127 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-784-7_11 DO - 10.2991/978-94-6463-784-7_11 ID - Khan2025 ER -