Indoor Objects Segmentation Dataset

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Project Overview:

Objective

Our team has embarked on a mission to create the Indoor Objects Segmentation Dataset, uniquely designed for segmenting indoor objects. This dataset is pivotal for innovations in interior design software, augmenting reality applications, and enhancing robotic navigation in indoor settings. Furthermore, it will enable smoother transitions between different tasks, fostering seamless integration into various technologies. With an active voice, we are diligently crafting a resource that will revolutionize how indoor environments are understood and interacted with, driving forward advancements in multiple fields.

Scope

We embarked on our approach by gathering a vast array of indoor scenes. Subsequently, we meticulously annotated them to identify and classify a variety of common indoor objects.

Sources

Certainly! Could you please provide the specific content you’d like to be revised? This will help me add transition words and convert the content to an active voice effectively.
Furniture catalog and brochure imagery. Moreover, the use of high-quality materials ensures both durability and elegance.
Legally permitted indoor security camera footage, which is recorded within the bounds of the law, serves as valuable evidence for various purposes. For instance, businesses use this footage to monitor employee activities and ensure safety protocols are followed. Additionally, homeowners rely on these cameras to deter burglars and monitor their property.
Diverse 3D-rendered synthetic scenes. Moreover, architects can present detailed visualizations of their designs to clients, providing a clearer understanding of the final product.

Data Collection Metrics

Total Images Gathered:330,000
Residential Interiors: 132,000
Office Spaces: 99,000
Catalog and Brochures: 55,000
Security Camera Footage: 22,000
3D-Rendered Scenes: 22,000
Total Annotations Made: 1,320,000

Annotation Process

Stages

Image Quality Assessment: We carefully assessed image quality, ensuring that each image met our high standards for clarity, lighting, and resolution.
Object Detection: Next, we focused on object detection, identifying various objects within each scene.
Segmentation: Then, we executed pixel-perfect segmentation for each object.
Classification: Finally, we precisely labeled every segmented object, such as sofas, tables, and lamps.

Annotation Metrics

Total Annotations: 1,200,000
Images Quality-Assessed: 300,000
Objects Detected: 900,000
Segmentations Completed: 900,000
Objects Classified: 900,000

Quality Assurance

Stages

Expert Review: To enhance our process, we have incorporated several key strategies. First, we prioritize expert reviews by collaborating closely with interior designers and architects. They help us evaluate a random subset of our annotations, ensuring accuracy and professional insight.
Automated Overlap Checks: Additionally, we employ advanced algorithms for automated overlap checks. These sophisticated tools guarantee realistic segmentation, preventing any unnatural overlaps that could compromise the quality of our data.
Inter-annotator Agreement: Furthermore, we conduct rigorous consistency checks through inter-annotator agreement. By having multiple annotators review overlapping data segments, we ensure a high level of accuracy and uniformity in our annotations.

QA Metrics

Expert-Reviewed Annotations: 132,000 (10% of total)
Overlaps Identified and Corrected: 19,800 (1.5% of total)

Conclusion

The Indoor Objects Segmentation Dataset stands as a testament to our dedication and expertise in data collection and annotation. Consequently, it’s designed to be a benchmark in indoor object segmentation, addressing the critical needs of AR, interior design, and robotics sectors. Moreover, our meticulous approach guarantees a dataset of the highest quality, setting new standards in the field. By consistently prioritizing quality, we ensure that our Indoor Objects Segmentation Dataset not only meets but exceeds industry expectations. Additionally, our team’s commitment to excellence is reflected in every aspect of the dataset, from data collection to annotation.