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Sketch/Image-Based 3D Scene Retrieval: Benchmark, Algorithm, Evaluation

机译:素描/基于图像的3D场景检索:基准,算法,评估

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Sketch/Image-based 3D scene retrieval is to retrieve man-made 3D scene models given a user's hand-drawn 2D scene sketch or a 2D scene image usually captured by a camera. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from either non-realistic 2D scene sketches or realistic 2D scene images. Due to the intuitiveness in sketching and ubiquitous availability in image capturing, this research topic has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR entertainment. To boost this interesting and important research, we build the currently largest and most comprehensive 2D scene sketch/image-based 3D scene retrieval benchmark1, develop a convolutional neural network (CNN)-based 3D scene retrieval algorithm and finally conduct an evaluation on the benchmark.
机译:基于素描/图像的3D场景检索是给定人制造的3D场景模型给定用户手绘2D场景草图或通常由相机捕获的2D场景图像。它是一个全新的,但也是在3D对象检索领域的一个新的,但在3D对象检索领域,由于它们的陈述中的语义间隙:3D场景模型或视图与非现实2D场景草图或现实的2D场景图像不同。由于在图像捕获中的素描和无处不在的可用性方面的直观,该研究主题具有广大应用,如3D场景重建,自动驾驶汽车,3D几何视频检索和3D AR / VR娱乐。为了提高这种有趣和重要的研究,我们构建了目前最大,最全面的2D场景草图/图像的3D场景检索基准,开发了一种卷积神经网络(CNN)基础的3D场景检索算法,最后对基准进行了评估。

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