The evaluation of street view perception plays a crucial role in the design and improvement of urban street layouts by urban planners. However, the most commonly used method for evaluating street views by planners and researchers is through questionnaires, which may not effectively capture users' psychological feelings. Currently, there is a lack of research on providing suggestions for improving street views that score low in perception evaluation. To address this issue, we propose a method for highlighting areas where street views are insufficient in six aspects which are beautiful, safety, wealthy, lively, boring and depressing to provide suggestions for improvement. Reference pictures and corresponding improvements suggested by the proposed system are necessary as they can provide urban planners with intuitive suggestions for street improvement. In this study, we recruit volunteers to rate street view maps in the six aspects mentioned above. We then use the scene graph generation method to characterize the relationship between objects in the street view images. Finally, we apply the graph-matching algorithm SimGNN to identify three pictures that are highly similar to the graph structure of the high-score street views as reference images. This approach effectively provides suggestions for street view images with low-score, while also offering an intuitive way to improve street view perception for urban planners. Overall, our proposed method provides a more comprehensive and effective way to evaluate and improve street views, which can contribute to better urban planning and design.
KEYWORDS: 3D modeling, Clouds, Data modeling, Human-machine interfaces, Cognitive modeling, Interfaces, 3D acquisition, Image retrieval, Image filtering, 3D image processing
3D modeling based on point clouds is an efficient way to reconstruct and create detailed 3D content. However, the geometric procedure may lose accuracy due to high redundancy and no explicit structure. In this work, we propose a human-in-the-loop sketch-based point clouds reconstruction framework to leverage the users’ cognitive ability in geometry extraction. We present an interactive drawing interface for 3D model creations from point cloud data with the help of user sketches. We adopt an optimization method that the user can continuously edit the contours extracted from the obtained 3D model and retrieve the model iteratively. Finally, we verify the proposed user interface for modeling from sparse point clouds.
With the emergence of large-scale open online courses and online academic conferences, it has become increasingly feasible and convenient to access online educational resources. However, it is time consuming and challenging to effectively retrieve and present numerous lecture videos for common users. In this work, we propose a hierarchical visual interface for retrieving and summarizing lecture videos. Users can utilize the proposed interface to effectively explore the required video information through the results of the video summary generation in different layers. We retrieve the input keywords with the corresponding video layer with timestamps, a frame layer with slides, and the poster layer with summarization of the lecture videos. We verified the proposed interface with our user study by comparing it with other conventional interfaces. The results from our user study confirmed that the proposed interface can achieve high retrieval accuracy and good user experience.
Normal map is an important and efficient way to represent complex 3D models. A designer may benefit from the auto-generation of high quality and accurate normal maps from freehand sketches in 3d content creation. This paper proposes a deep generative model for generating normal maps from users’ sketch with geometric sampling. Our generative model is based on conditional generative adversarial network with the curvature-sensitive points sampling of conditional masks. This sampling process can help eliminate the ambiguity of generation results as network input. It is verified that the proposed framework can generate more accurate normal maps.
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