Główne pojęcia
This paper introduces SCRREAM, a novel framework for creating highly accurate and dense 3D annotations of indoor scenes, addressing limitations in existing datasets that prioritize scale over detailed geometry.
Streszczenie
SCRREAM: A Framework and Benchmark for Annotating Dense 3D Indoor Scenes
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:
- Scan: Individual objects and the empty room are scanned in high resolution to create watertight meshes.
- Register: Objects are placed in the scene, and a partial scan helps register the pre-scanned meshes to the scene layout.
- Render: The registered scene is rendered realistically using Blender, generating synthetic views with known camera poses.
- Mapping: A multi-modal camera rig captures real image sequences, and a modified Structure from Motion (SfM) method aligns these sequences with the rendered views, obtaining accurate camera poses relative to the scene.
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.
Statystyki
The authors provide 11 scenes comprising 7114 frames, 94 object & furniture meshes, and 7 indoor room meshes for the indoor reconstruction and SLAM dataset.
An additional 9323 frames across 8 scenes are provided for object removal and scene editing tasks.
Two scenes are presented for semi-dynamic human reconstruction using a mannequin.
Two scenes are showcased for 6D object pose estimation.
Cytaty
"Traditionally, 3D indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details."
"In this paper, we propose SCRREAM, a dataset annotation framework that allows annotation of fully dense meshes of objects in the scene and registers camera poses on the real image sequence, which can produce accurate ground truth for both sparse 3D as well as dense 3D tasks."
"Our dataset is the only dataset to our knowledge with such an accurate setup covering the indoor room with a hand-held camera. This uniquely allows in-depth geometric evaluation and benchmarking of methods for most popular 3D applications such as NVS and SLAM."