Introduction to RGBD Semantic Segmentation
RGBD semantic segmentation is an intriguing realm of computer vision that incorporates both color (RGB) and depth (D) information into image segmentation tasks. This field, instrumental in a variety of applications from robotic vision to scene understanding, involves categorizing different parts of an image into predefined classes based on their semantic content using both color and spatial data.
Project Background
This project focuses on compiling a comprehensive list of papers related to RGBD semantic segmentation, making it an invaluable resource for researchers and practitioners. The collection is frequently updated, reflecting the rapid progress and evolving nature of the field. The project was last updated in October 2023, showcasing recent advancements and contributions.
Datasets
The project highlights several critical datasets that are commonly used in this area:
- NYUDv2: A dataset featuring 1449 RGBD images from indoor scenes, segmented into 40 categories.
- SUN RGB-D: Comprising 10,335 images spread across 37 categories, this dataset offers a rich source for training and testing.
- 2D-3D-Semantic (Stanford): Includes a large set of annotated RGB and depth images with 13 object categories.
- Cityscapes: Known for high-quality annotations, this dataset is crucial for studies focused on urban street scenes.
- ScanNet: An extensive RGBD video dataset that provides annotated scans with 3D camera poses and semantic segmentations.
Metrics
Performance evaluation in RGBD semantic segmentation is often centered around key metrics such as:
- Pixel Accuracy (PixAcc)
- Mean Accuracy (mAcc)
- Mean Intersection over Union (mIoU)
- Frequency Weighted IOU (f.w.IOU)
These metrics assess the precision of segmentation, comparing results against ground truth data to determine effectiveness.
Performance Tables
The project provides comparative performance tables for different methods on various datasets. Comparisons are based on indexing metrics previously mentioned (PixAcc, mAcc, mIoU, and f.w.IOU). The goal is to achieve a result closest to the ground truth, with higher index values indicating superior performance.
Conclusion
The RGBD semantic segmentation project serves as an essential repository for anyone involved in computer vision, offering insights into the progress and innovations within the field. By consolidating research updates, dataset details, and performance comparisons, it fosters advancements in applications that require nuanced understanding of both color and depth data.
This project is continuously evolving, reflecting the dynamic nature of technological advancements and research endeavors in image segmentation, making it a pivotal point of reference in the area of RGBD semantic segmentation.