Automatic Semantic Modeling of Indoor Scenes from Low-quality RGB-D Data using Contextual Information
Automatic Semantic Modeling of Indoor Scenes from Low-quality RGB-D Data using Contextual Information
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DOI：
10.1145/2661229.2661239
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发表时间：
2014-11-01
影响因子：
6.2
通讯作者：
Hu, Shi-Min
中科院分区：
文献类型：
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作者：
Chen, Kang;Lai, Yu-Kun;Hu, Shi-Min
我们提出了一种新的解决方案，从稀疏的低质量的RGB-D图像集的室内场景的自动语义建模。这些数据由于噪声、低分辨率、遮挡和缺失深度信息而带来挑战。我们利用场景数据库中的知识，其中包含100多个室内场景，超过10，000个手动分割和标记的对象网格模型。在几秒钟内，我们输出了一个视觉上合理的3D场景，调整这些模型及其部件以适应输入扫描。从数据库中学习的上下文关系用于约束重建，确保对象模型和部件之间的语义兼容性。小的对象和对象的深度信息不完整，难以可靠地恢复的两个阶段的方法进行处理。首先识别主要对象，提供已知的场景结构。然后使用基于2D轮廓的模型检索来恢复较小的对象。使用我们自己的数据和两个公共数据集的评估表明，我们的方法可以建模典型的真实世界的室内场景有效和强大。
We present a novel solution to automatic semantic modeling of indoor scenes from a sparse set of low-quality RGB-D images. Such data presents challenges due to noise, low resolution, occlusion and missing depth information. We exploit the knowledge in a scene database containing 100s of indoor scenes with over 10,000 manually segmented and labeled mesh models of objects. In seconds, we output a visually plausible 3D scene, adapting these models and their parts to fit the input scans. Contextual relationships learned from the database are used to constrain reconstruction, ensuring semantic compatibility between both object models and parts. Small objects and objects with incomplete depth information which are difficult to recover reliably are processed with a two-stage approach. Major objects are recognized first, providing a known scene structure. 2D contour-based model retrieval is then used to recover smaller objects. Evaluations using our own data and two public datasets show that our approach can model typical real-world indoor scenes efficiently and robustly.