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

机译:基于草图/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:基于草图/图像的3D场景检索是在给定用户手绘的2D场景草图或通常由相机捕获的2D场景图像的情况下,检索人造3D场景模型。由于3D对象检索中的语义差异,它是3D对象检索领域中一个崭新的但也是非常具有挑战性的研究主题:3D场景模型或视图不同于非真实的2D场景草图或真实的2D场景图像。由于草图的直观性和图像捕捉的普遍可用性,该研究主题具有广泛的应用,例如3D场景重建,自动驾驶汽车,3D几何视频检索和3D AR / VR娱乐。为了推动这项有趣而重要的研究,我们建立了目前最大,最全面的基于2D场景草图/图像的3D场景检索基准1,开发了基于卷积神经网络(CNN)的3D场景检索算法,并最终对该基准进行了评估。

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