mechanism 发表于 2025-3-25 05:35:13
http://reply.papertrans.cn/16/1502/150104/150104_21.pngmunicipality 发表于 2025-3-25 08:30:23
3D Visual Object Detection from Monocular ImagesWe train a convolutional neural network (CNN) on our feature map leveraging bounding boxes annotated on corresponding LiDAR scans. Experiments show that our method performs favorably against baselines.暂时中止 发表于 2025-3-25 15:04:32
Delineation of Road Networks Using Deep Residual Neural Networks and Iterative Hough Transformchnique for vectorizing the rasterized segmentation result, removing erroneous lines, and refining the road network. The result is a set of vectors representing the road network. We have extensively tested the proposed pipeline and provide quantitative and qualitative comparisons with other state-of-the-art based on a number of known metrics.心神不宁 发表于 2025-3-25 17:56:16
Bioinspired Simulation of Knotting Hagfish In this paper, we present the first physics-based simulation of the knot-sliding behavior of hagfish. We show that a contact-based inverse dynamics approach, motivated by the biological concept called ., works very well for this challenging control problem.允许 发表于 2025-3-25 20:04:28
http://reply.papertrans.cn/16/1502/150104/150104_25.pngCUB 发表于 2025-3-26 01:55:21
http://reply.papertrans.cn/16/1502/150104/150104_26.png同步信息 发表于 2025-3-26 06:32:31
Morton Schatzman,Alan Worsley,Peter Fenwickta Communication. Using these examples, we show that our approach is more general than the current state of the art, and that there are significant similarities between several domains in need of interactive visualization, which are mostly treated as completely separate.NUL 发表于 2025-3-26 11:11:26
http://reply.papertrans.cn/16/1502/150104/150104_28.png率直 发表于 2025-3-26 16:15:59
http://reply.papertrans.cn/16/1502/150104/150104_29.png同来核对 发表于 2025-3-26 18:21:35
Conference proceedings 2019ober 2019...The 100 papers presented in this double volume were carefully reviewed and selected from 163 submissions. The papers are organized into the following topical sections: Deep Learning I; Computer Graphics I; Segmentation/Recognition; Video Analysis and Event Recognition; Visualization; ST: