Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds

Yao, Yasuhiro; Xu, Katie; Murasaki, Kazuhiko; Ando, Shin; Sagata, Atsushi

doi:10.1186/s41074-020-00064-w

Cited by 5 publications

(7 citation statements)

References 23 publications

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“…Although a high threshold will result in more accurate pseudo-labels, it will lead to a decrease in the number of labeled points. However, Yao et al (2020) found that the experimental results are not sensitive to the threshold setting. Unlike using a fixed value, we developed an adaptive threshold-setting method.…”

Section: Generation Of Pseudo-labelsmentioning

confidence: 93%

“…Unlike the setting in Yao et al (2020), the model that converges on the ground truth is used as the condition for updating pseudo labels. We believe that there are some errors in pseudo labels, particularly when the scene is complex, thus completely fitting pseudo-labels may result in error transmission.…”

Section: Update Of Pseudo Labelsmentioning

confidence: 99%

“…Only a few studies have utilized pseudo-labels in point cloud processing. Yao et al (2020) introduced a pseudo-labeling method into point cloud semantic segmentation. However, similar to Guinard and Landrieu (2017), the framework is also transductive learning , and the performance of the model has not been verified on untrained test data.…”

Section: Introductionmentioning

confidence: 99%

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Weakly Supervised Pseudo-Label Assisted Learning for Als Point Cloud Semantic Segmentation

Wang

Yao

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Competitive point cloud semantic segmentation results usually rely on a large amount of labeled data. However, data annotation is a time-consuming and labor-intensive task, particularly for three-dimensional point cloud data. Thus, obtaining accurate results with limited ground truth as training data is considerably important. As a simple and effective method, pseudo labels can use information from unlabeled data for training neural networks. In this study, we propose a pseudo-label-assisted point cloud segmentation method with very few sparsely sampled labels that are normally randomly selected for each class. An adaptive thresholding strategy was proposed to generate a pseudo-label based on the prediction probability. Pseudo-label learning is an iterative process, and pseudo labels were updated solely on ground-truth weak labels as the model converged to improve the training efficiency. Experiments using the ISPRS 3D sematic labeling benchmark dataset indicated that our proposed method achieved an equally competitive result compared to that using a full supervision scheme with only up to 2‰ of labeled points from the original training set, with an overall accuracy of 83.7% and an average F1 score of 70.2%.

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Section: Generation Of Pseudo-labelsmentioning

confidence: 93%

Section: Update Of Pseudo Labelsmentioning

confidence: 99%

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Weakly Supervised Pseudo-Label Assisted Learning for Als Point Cloud Semantic Segmentation

Wang

Yao

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A generative model via adversarial training approximates the actual data manifold. The adopted approach tries to distinguish the relationship between the unlabeled data and the already labelled data sample using intermediate feature representations in deep neural network [27]. In recent research [28,29] on person re-identification, an approach of multi-pseudo labels on the generated data was used to reduce the risk of overfitting.…”

Section: Pseudo-labellingmentioning

confidence: 99%

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Alkaabi¹,

Yussof²,

Almulla³

2023

Computers, Materials &Amp; Continua

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Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases. Traditionally, this process is done manually by human expert. However, the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness. To improve the recognition speed and consistency, researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network (CNN). CNN requires many training images to obtain high percentage of recognition accuracy. Unfortunately, it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts. A promising solution to this problem is a technique called Generative Adversarial Network (GAN). GAN is a technique that can generate synthetic images that has similar statistics as the training set. A variation of GAN called Conditional GAN (CGAN) enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated. This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation. We also propose a pseudolabelling technique to label the generated images with proper age and gender. We used the combination of real and generated images to train Dental Age and Sex Net (DASNET), which is a CNN model for dental age estimation. Based on the experiment conducted, the accuracy, coefficient of determination (R2) and Absolute Error (AE) of DASNET have improved to 87%, 0.85 and 1.18 years respectively as opposed to 74%, 0.72 and 3.45 years when DASNET is trained using real, but smaller number of images.

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“…The first is label propagation, a typical technique to generate pseudo labels. Yao et al [26] combined pseudo labelling with PointNet and proposed a process that alternates between classification network training and label propagation to generate final semantic labelling. Nonetheless, this framework employs transductive learning, and the model's performance remains debatable.…”

Section: Generate Pseudo Labelsmentioning

confidence: 99%

A survey on weakly supervised 3D point cloud semantic segmentation

Wang,

Liu,

Tan

et al. 2023

IET Computer Vision

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With the popularity and advancement of 3D point cloud data acquisition technologies and sensors, research into 3D point clouds has made considerable strides based on deep learning. The semantic segmentation of point clouds, a crucial step in comprehending 3D scenes, has drawn much attention. The accuracy and effectiveness of fully supervised semantic segmentation tasks have greatly improved with the increase in the number of accessible datasets. However, these achievements rely on time‐consuming and expensive full labelling. In solve of these existential issues, research on weakly supervised learning has recently exploded. These methods train neural networks to tackle 3D semantic segmentation tasks with fewer point labels. In addition to providing a thorough overview of the history and current state of the art in weakly supervised semantic segmentation of 3D point clouds, a detailed description of the most widely used data acquisition sensors, a list of publicly accessible benchmark datasets, and a look ahead to potential future development directions is provided.

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Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds

Cited by 5 publications

References 23 publications

Weakly Supervised Pseudo-Label Assisted Learning for Als Point Cloud Semantic Segmentation

Weakly Supervised Pseudo-Label Assisted Learning for Als Point Cloud Semantic Segmentation

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

A survey on weakly supervised 3D point cloud semantic segmentation

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