{"id":1157,"date":"2023-12-08T14:58:57","date_gmt":"2023-12-08T14:58:57","guid":{"rendered":"https:\/\/www.gdsig.cn\/?gdsig=257"},"modified":"2023-12-08T14:58:57","modified_gmt":"2023-12-08T14:58:57","slug":"post-257","status":"publish","type":"post","link":"https:\/\/test.gdsig.cn\/index.php\/2023\/12\/08\/post-257\/","title":{"rendered":"\u5173\u4e8e\u53ec\u5f00\u5e7f\u4e1c\u7701\u56fe\u8c61\u56fe\u5f62\u5b66\u4f1a\u5b66\u672f\u5e74\u4f1a\u66a8\u7b2c\u5341\u4e09\u5c4a\u7ca4\u6e2f\u6fb3\u56fe\u50cf\u56fe\u5f62\u5b66\u672f\u4f1a\u8bae\u7684\u901a\u77e5"},"content":{"rendered":"

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\u8bb2\u8005\u7b80\u4ecb<\/span>\uff1a<\/span><\/strong><\/span><\/span><\/strong>\u4efb\u4f20\u8d24\uff0c\u4e2d\u5c71\u5927\u5b66\u6570\u5b66\u5b66\u9662\u6559\u6388\u3001\u526f\u9662\u957f\uff0c\u79d1\u5b66\u8ba1\u7b97\u4e0e\u8ba1\u7b97\u673a\u5e94\u7528\u7cfb\u4e3b\u4efb\uff0c\u5e7f\u4e1c\u7701\uff08\u5e7f\u5dde\uff09\u5de5\u4e1a\u4e0e\u5e94\u7528\u6570\u5b66\u5b66\u4f1a\u526f\u7406\u4e8b\u957f\u517c\u79d8\u4e66\u957f\uff0c\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u8ba1\u7b97\u673a\u89c6\u89c9\u4e13\u59d4\u4f1a\u526f\u79d8\u4e66\u957f\uff0c\u4e2d\u5c71\u5927\u5b66<\/span>\u201c\u9038\u4ed9\u5b66\u8005\u201d\u5c97\u4f4d\u6559\u6388\u3002\u957f\u671f\u5173\u6ce8\u9ad8\u7ef4\u3001\u5f02\u8d28\u89c6\u89c9\u6570\u636e\u7684\u7279\u5f81\u8868\u8fbe\u4e0e\u5b66\u4e60\u7b97\u6cd5\uff0c\u5728\u591a\u5c3a\u5ea6\u5224\u522b\u5206\u6790\u3001\u7a00\u758f\u8868\u793a\u4e0e\u6b63\u5219\u5316\u65b9\u6cd5\u3001\u6df1\u5ea6\u81ea\u9002\u5e94\u7279\u5f81\u8868\u8fbe\u7b49\u65b9\u9762\u5f00\u5c55\u4e86\u8f83\u4e3a\u7cfb\u7edf\u7684\u7814\u7a76\u3002\u4e3b\u6301\u5e7f\u4e1c\u7701\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u6770\u51fa\u9752\u5e74\u9879\u76ee\uff0c\u4e3b\u6301\u548c\u53c2\u52a0\u591a\u9879\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u9879\u76ee\uff0c\u5728\u56fd\u9645\u91cd\u8981\u5b66\u672f\u671f\u520aIEEE TPAMI, TIP, TNNLS, TMI, TCYB\u4ee5\u53ca\u91cd\u8981\u5b66\u672f\u4f1a\u8baeCVPR\u3001AAAI\u3001MICCAI\u7b49\u53d1\u8868\u8bba\u6587\u4e94\u5341\u4f59\u7bc7\uff0c\u83b7\u5f972015\u5e74\u5ea6\u6559\u80b2\u90e8\u81ea\u7136\u79d1\u5b66\u7814\u7a76\u4f18\u79c0\u6210\u679c\u4e8c\u7b49\u5956\u548c2022\u5e74\u5ea6\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a\u81ea\u7136\u79d1\u5b66\u4e8c\u7b49\u5956\u3002<\/span><\/p>\n

\u62a5\u544a\u6458\u8981\uff1a<\/span><\/strong>\u968f\u7740\u5927\u6570\u636e\u4e0e\u4fe1\u606f\u6280\u672f\u7684\u4e0d\u65ad\u8fdb\u6b65\uff0c\u673a\u5668\u89c6\u89c9\u7684\u5b66\u4e60\u65b9\u6cd5\u53d6\u5f97\u4e86\u5feb\u901f\u53d1\u5c55\u3002\u7136\u800c\uff0c\u8fd9\u4e9b\u6210\u529f\u5f80\u5f80\u4e25\u91cd\u4f9d\u8d56\u4e8e\u6570\u636e\u8d28\u91cf\u548c\u6a21\u578b\u5047\u8bbe\uff0c\u800c\u6d77\u91cf\u6570\u636e\u5e26\u6765\u7684\u590d\u6742\u7279\u6027\u4f7f\u5f97\u4f9d\u8d56\u7684\u7406\u60f3\u6761\u4ef6\u96be\u4ee5\u6ee1\u8db3\uff0c\u5bfc\u81f4\u7ecf\u5178\u7684\u673a\u5668\u5b66\u4e60\u7406\u8bba\u4e0e\u7b97\u6cd5\u5931\u6548\u3002\u4e00\u65b9\u9762\uff0c\u7ecf\u5178\u5b66\u4e60\u7406\u8bba\u5f80\u5f80\u5047\u8bbe\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u670d\u4ece\u540c\u5206\u5e03\uff0c\u800c\u8de8\u73af\u5883\u83b7\u53d6\u7684\u73b0\u5b9e\u6570\u636e\u5374\u666e\u904d\u5b58\u5728\u7ed3\u6784\u5f02\u8d28\u3001\u5206\u5e03\u5dee\u5f02\u7b49\u95ee\u9898\uff0c\u5bfc\u81f4\u6cdb\u5316\u8bef\u5dee\u754c\u8fc7\u4e8e\u5bbd\u677e\uff0c\u5b66\u4e60\u6a21\u578b\u6b20\u7f3a\u7406\u8bba\u4fdd\u969c\uff1b\u53e6\u4e00\u65b9\u9762\uff0c\u4f20\u7edf\u673a\u5668\u89c6\u89c9\u65b9\u6cd5\u5ffd\u7565\u4e86\u6570\u636e\u5206\u5e03\u5dee\u5f02\u7684\u523b\u753b\uff0c\u7f3a\u5c11\u7406\u8bba\u6027\u8d28\u826f\u597d\u4e14\u5e94\u7528\u9ad8\u6548\u7684\u5dee\u5f02\u5ea6\u91cf\uff0c\u4ece\u800c\u96be\u4ee5\u4fdd\u8bc1\u89c6\u89c9\u8868\u5f81\u7684\u53ef\u6cdb\u5316\u6027\uff0c\u4e3a\u89c6\u89c9\u56fe\u50cf\u7684\u81ea\u9002\u5e94\u7279\u5f81\u8868\u8fbe\u5e26\u6765\u4e86\u4e25\u5cfb\u7684\u6311\u6218\u3002\u672c\u6b21\u62a5\u544a\u5c06\u5411\u540c\u884c\u6c47\u62a5\u6570\u636e\u504f\u5dee\u573a\u666f\u4e0b\u7684\u6cdb\u5316\u8bef\u5dee\u7406\u8bba\u4e0e\u89c6\u89c9\u4e0d\u53d8\u8868\u5f81\u5b66\u4e60\u65b9\u6cd5\uff0c\u4ee5\u53ca\u57fa\u4e8e\u6700\u4f18\u4f20\u8f93\u539f\u7406\u7684\u51e0\u4f55\u5efa\u6a21\u4e0e\u89e3\u91ca\u3002<\/span><\/p>\n

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\u7279\u9080\u62a5\u544a4\uff1a<\/span><\/strong>Empowing Deep Modeling of 3D Point Clouds with 2D Representation Modalities<\/span><\/strong><\/span><\/p>\n

\"image.png\"<\/span><\/strong><\/p>\n

\u8bb2\u8005\u7b80\u4ecb<\/span>\uff1a<\/span><\/strong><\/span><\/span><\/strong>Prof. Junhui Hou is an Associate Professor with the Department of Computer Science, City University of Hong Kong. Prof. Hou’s research interests include\u00a0multi-dimensional visual computing, such as image\/video\/3D geometry representation, processing and analysis,\u00a0graph structure learning, and data compression. \u00a0He received the Early Career Award (3\/381) from the Hong Kong Research Grants Council in 2018. He is an elected member of IEEE MSA-TC, IEEE VSPC-TC, and IEEE MMSP-TC. He is currently an Associate Editor for IEEE Transactions on Image Processing, IEEE Transactions on Visualization and Computer Graphics, and IEEE Transactions on Circuits and Systems for Video Technology.<\/span><\/p>\n

\u62a5\u544a\u6458\u8981\uff1a<\/span><\/strong>3D point cloud data is becoming increasingly popular in various emerging applications, such as meta-verse, autonomous driving, and computer animations\/games, as it provides an explicit representation of the geometric structures of objects and scenes. While deep learning has achieved great success in 2D image and\u00a0video processing, it is still in its infancy for 3D point cloud data, which is a fast-growing but challenging field due to its irregular and unstructured characteristics. That is, designing deep architectures and loss functions for 3D point cloud data is difficult, and as a result, the representation capability of existing deep architectures is limited. In this talk, I will present our efforts to advance this field from two perspectives. First, we explore the representation of 3D point clouds as 2D images, where mature 2D CNNs can be applied. Second, we investigate the possibility of transferring visual knowledge from 2D images to 3D point clouds. We conduct extensive experiments on various low- and high-level tasks to demonstrate the advantages of these approaches. Such new perspectives will open up many possibilities in deep 3D point cloud data modeling.<\/span><\/p>\n

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\u00a0<\/span><\/p>\n

\u5927\u4f1a\u8054\u7cfb\u4eba<\/span><\/strong><\/span><\/p>\n

\u00a0\u5f90\u8001\u5e08\uff0c<\/span><\/span>Email:\u00a0<\/span>laterfall@hit.edu.cn<\/span><\/a><\/p>\n

\u00a0 \u00a0 \u90d1\u8001\u5e08<\/span>\uff0cE<\/span><\/span>mail:\u00a0<\/span>wszheng@outlook.com<\/a>\u00a0<\/span><\/p>\n

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