Efficient 3D Scene Semantic Segmentation via Active Learning on Rendered 2D Images

Article Engineering, Electrical & Electronic

RGB-D Semantic Segmentation and Label-Oriented Voxelgrid Fusion for Accurate 3D Semantic Mapping

Wenjun Shi et al.

Summary: This paper presents a methodology for obtaining 3D semantic maps from RGB-D scans, focusing on accurate 2D frame labeling and semantic fusion. The proposed method achieves steady semantic segmentation through a two-stream network and introduces a discriminatory mask loss. The method also ensures spatial continuity and spatiotemporal consistency by using a label-oriented voxelgrid filter and an adaptive grouping algorithm.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2022)

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Article Geography, Physical

A new weakly supervised approach for ALS point cloud semantic segmentation

Puzuo Wang et al.

Summary: In this study, a deep-learning-based weakly supervised framework is proposed for the semantic segmentation of ALS point clouds. The method utilizes implicit information from unlabeled data with incomplete and sparse labels, and introduces entropy regularization, consistency constraint, and an online soft pseudo-labeling strategy to improve classification performance.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2022)

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Article Engineering, Electrical & Electronic

Active Learning Based 3D Semantic Labeling From Images and Videos

Mengqi Rong et al.

Summary: In this paper, an active learning based 3D semantic labeling method is proposed to generate accurate 3D semantic mesh models by integrating 2D semantic segmentation results and 3D mesh models. Through the iterative process of training, fusion, and selection, the labeling quality is improved while reducing the amount of annotation needed.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2022)

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Article Computer Science, Theory & Methods

A Survey of Deep Active Learning

Pengzhen Ren et al.

Summary: Researchers have shown relatively lower interest in active learning compared to deep learning, but with the increasing demand for large-scale high-quality annotated datasets, active learning is receiving more attention. This article provides a comprehensive survey on deep active learning, including a formal classification method, an overview of existing work, and an analysis of developments from an application perspective.

ACM COMPUTING SURVEYS (2022)

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Article Computer Science, Artificial Intelligence

Deep Learning for 3D Point Clouds: A Survey

Yulan Guo et al.

Summary: Recently, deep learning on point clouds has been gaining more attention, but it is still in its early stages due to the unique challenges faced in processing point clouds with deep neural networks.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Article Geography, Physical

SUM: A benchmark dataset of Semantic Urban Meshes

Weixiao Gao et al.

Summary: Recent advancements in data acquisition technology have enabled rapid collection of 3D texture meshes, aiding in urban environment analysis and planning. Semantic segmentation through deep learning enhances understanding but demands a significant amount of labelled data. This research introduces a new benchmark dataset, semi-automatic annotation framework, and annotation tool for 3D meshes, offering potential time savings and comparative analysis for semantic segmentation methods.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

All you need are a few pixels: semantic segmentation with PIXELPICK

Gyungin Shin et al.

Summary: The study demonstrates that good segmentation performance can be achieved with only a few well-chosen pixel labels, and proposes an active learning framework to significantly reduce labeling cost. Extensive experiments show that our approach performs comparably with existing techniques in low annotation regimes.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

Qingyong Hu et al.

Summary: This paper introduces an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which covers large areas from three UK cities. The performance of state-of-the-art algorithms on this dataset is extensively evaluated, and a comprehensive analysis of the results is provided, identifying key challenges towards urban-scale point cloud understanding.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

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Article Computer Science, Software Engineering