CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

Chen, Runnan; Liu, Youquan; Kong, Ling-Dong; Zhu, Xinge; Ma, Yuexin; Li, Yikang; Hou, Yue; Qiao, Yu; Wang, Wenping

doi:10.48550/arxiv.2301.04926

Cited by 2 publications

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“…Also, the construction of specific knowledge distillation optimisation algorithms for point cloud-based pure 3D detection has not been thoroughly investigated [ 55 ]. This method is heavily reliant on a huge collection of annotated point clouds, which is especially important when high-quality 3D annotations are expensive to get [ 56 ].…”

Section: Segmentationmentioning

confidence: 99%

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Vinodkumar

Karabulut

Avots

et al. 2023

Entropy

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The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.

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Section: Segmentationmentioning

confidence: 99%

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Vinodkumar

Karabulut

Avots

et al. 2023

Entropy

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“…SDS Complete outperforms existing approaches on objects not well-represented in training datasets, demonstrating its efficacy in handling incomplete real-world data. Paper [85] presents CLIP2Scene, a framework that transfers knowledge from pre-trained 2D image-text models to a 3D point cloud network. Using a semantics-based multimodal contrastive learning framework, the authors achieve annotation-free 3D semantic segmentation with significant mIoU scores on multiple datasets, even with limited labeled data.…”

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confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

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The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

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CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

Cited by 2 publications

References 0 publications

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Localization and Mapping for Self-Driving Vehicles: A Survey

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