Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB‐D Camera

Kim, Hyomin; Kim, Jungeon; Nam, Hyeonseo; Park, Jaesik; Lee, Seungyong

doi:10.1111/cgf.142652

Cited by 1 publication

(1 citation statement)

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“…As the most commonly used sensor for data acquisition in 2D or 3D object detection and semantic segmentation tasks, the monocular camera provides RGB images rich in texture information [2,36,86]. Specifically, for every image pixel as (u, v), it has a multiple channel feature vector as F (u,v) = {R, G, B, ...} which usually contains the camera capture color decomposing in the red, blue, green channel or other manually designed feature as the gray-scale channel.…”

Section: Image Representationmentioning

confidence: 99%

Multi-modal Sensor Fusion for Auto Driving Perception: A Survey

Huang¹,

Shi²,

Li³

et al. 2022

Preprint

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Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers. However, achieving a rather good performance is not an easy task due to the noisy raw data, underutilized information, and the misalignment of multi-modal sensors. In this paper, we provide a literature review of the existing multi-modal-based methods for perception tasks in autonomous driving. Generally, we make a detailed analysis including over 50 papers leveraging perception sensors including LiDAR and camera trying to solve object detection and semantic segmentation tasks. Different from traditional fusion methodology for categorizing fusion models, we propose an innovative way that divides them into two major classes, four minor classes by a more reasonable taxonomy in the view of the fusion stage. Moreover, we dive deep into the current fusion methods, focusing on the remaining problems and open-up discussions on the potential research opportunities. In conclusion, what we expect to do in this paper is to present a new taxonomy of multi-modal fusion methods for the autonomous driving perception tasks and provoke thoughts of the fusion-based techniques in the future.

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Section: Image Representationmentioning

confidence: 99%