This research paper presents SCRREAM, a novel framework for generating high-fidelity 3D annotations of indoor scenes. The authors argue that existing datasets, while extensive, often lack the geometric accuracy required for evaluating tasks like depth rendering and scene understanding.
Research Objective: The paper aims to develop a framework capable of producing fully dense and accurate 3D annotations of indoor scenes, including object meshes, camera poses, and ground truth data for various vision tasks.
Methodology: SCRREAM employs a four-stage pipeline:
This framework allows for generating diverse datasets suitable for tasks like indoor reconstruction, object removal, human reconstruction, and 6D pose estimation.
Key Findings: The authors demonstrate the versatility of SCRREAM by creating datasets for the mentioned tasks. Notably, they provide benchmarks for novel view synthesis and SLAM using their accurately rendered depth ground truth, highlighting the superior performance achieved with their data compared to using noisy sensor data.
Main Conclusions: SCRREAM offers a significant advancement in 3D indoor scene annotation by prioritizing accuracy and completeness. The framework's ability to generate high-fidelity ground truth data makes it a valuable resource for evaluating and advancing 3D vision algorithms.
Significance: This research addresses a critical gap in 3D vision research by providing a method for creating datasets with precise geometric information. This contribution is crucial for developing and evaluating algorithms for applications like virtual and augmented reality, robotics, and scene understanding.
Limitations and Future Research: The authors acknowledge the complexity and time-consuming nature of their data acquisition process, limiting scalability. Future work could explore ways to streamline the pipeline and expand the dataset with more scenes and diverse human actions.
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by Hyun... às arxiv.org 10-31-2024
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