3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams

Zimmer, Walter; Rangesh, Akshay; Trivedi, Mohan M.

doi:10.1109/ivs.2019.8814071

Cited by 43 publications

(19 citation statements)

References 12 publications

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“…For example, the average number of instances in a scene in the ScanNet dataset [12] . the annotation cost is around 3 minutes 1 using off-the-shelf labeling tools [64], [7]. In contrast, it takes 22.3 minutes to conduct point-wise annotation [12] per scene.…”

Section: E Implementation Detailsmentioning

confidence: 99%

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

Liu¹,

Hu²,

Lei³

et al. 2022

Preprint

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Learning dense point-wise semantics from unstructured 3D point clouds with fewer labels, although a realistic problem, has been under-explored in literature. While existing weakly supervised methods can effectively learn semantics with only a small fraction of point-level annotations, we find that the vanilla bounding box-level annotation is also informative for semantic segmentation of large-scale 3D point clouds. In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision. The key to our approach is to generate accurate pseudo labels by exploring the geometric and topological structure inside and outside each bounding box. Specifically, an attention-based self-training (AST) technique and Point Class Activation Mapping (PCAM) are utilized to estimate pseudolabels. The network is further trained and refined with pseudo labels. Experiments on two large-scale benchmarks including S3DIS and ScanNet demonstrate the competitive performance of the proposed method. In particular, the proposed network can be trained with cheap, or even off-the-shelf bounding box-level annotations and subcloud-level tags.

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Section: E Implementation Detailsmentioning

confidence: 99%

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

Liu¹,

Hu²,

Lei³

et al. 2022

Preprint

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“…For this application, a few outdoor classes need to be (roughly) annotated. An overview and comparison of 33 annotation tools for this area of application is presented in [67]. These annotation tools mainly use simple geometries, such as bounding boxes, plane and lines, to form instances and semantic classes.…”

Section: Point Cloud Annotation Toolsmentioning

confidence: 99%

“…Feedback to annotators can be given directly by moving or highlighting the segment in the tool [26]. In addition, videos, teaching tools and abstract models are common and useful [67].…”

mentioning

confidence: 99%

Evaluating the Quality of Semantic Segmented 3D Point Clouds

Barnefske

Sternberg

2022

Remote Sensing

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Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms.

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“…Annotation tools on 2D image data have been very successful, and 3D annotation tools are also improving. 3D-Bat [11] and Latte [4] provide well-developed point cloud annotation tools integrated with semiautomatic functionalities, deployed as web-based applications. Latte realizes one-click annotation, significantly reducing complex annotation work into a simple click operation.…”

Section: B Annotation Tools For Point Cloudsmentioning

confidence: 99%

“…Frustum Pointnet [6], F-Convnet [7], and AVOD [8]) and semiautomatic annotation tools (e.g. PolygonRNN [9], Poly-gonRNN++ [10], 3D-bat [11], and Latte [4]). 3D-bat and Latte are specifically proposed for annotating 3D point cloud data.…”

Section: Introductionmentioning

confidence: 99%

SAnE: Smart Annotation and Evaluation Tools for Point Cloud Data

Arief

Guilin

et al. 2020

IEEE Access

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Addressing the need for high-quality, time efficient, and easy to use annotation tools, we propose SAnE, a semiautomatic annotation tool for labeling point cloud data. The contributions of this paper are threefold: (1) we propose a denoising pointwise segmentation strategy enabling a fast implementation of one-click annotation, (2) we expand the motion model technique with our guided-tracking algorithm, and (3) we provide an interactive, yet robust, open-source point cloud annotation tool, targeting both skilled and crowdsourcing annotators. Using the KITTI dataset, we show that the SAnE speeds up the annotation process by a factor of 4 while achieving Intersection over Union (IoU) agreements of 84%. Furthermore, in experiments using crowdsourcing services, SAnE achieves more than 20% higher IoU accuracy compared to the existing annotation tool and its baseline, while reducing the annotation time by a factor of 3. This result shows the potential of SAnE, for providing fast and accurate annotation labels for large-scale datasets with a significantly reduced price. SAnE is open-sourced at https://github.com/hasanari/sane. INDEX TERMS annotation tool, crowdsourcing annotation, frame tracking, point cloud data

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3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams

Cited by 43 publications

References 12 publications

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

Evaluating the Quality of Semantic Segmented 3D Point Clouds

SAnE: Smart Annotation and Evaluation Tools for Point Cloud Data

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