- ½¹µãÊÖÒÕ
- ÒÔÔ´´ÊÖÒÕϵͳΪ»ù±¾£¬£¬£¬£¬£¬£¬SenseCoreÉÌÌÀAI´ó×°ÖÃΪ½¹µã»ù×ù£¬£¬£¬£¬£¬£¬½á¹¹¶àÁìÓò¡¢¶àÆ«ÏòÇ°ÑØÑо¿£¬£¬£¬£¬£¬£¬
¿ìËÙÂòͨAIÔÚ¸÷¸ö±ÊÖ±³¡¾°ÖеÄÓ¦Ó㬣¬£¬£¬£¬£¬ÏòÐÐÒµ¸³ÄÜ¡£¡£¡£¡£¡£¡£¡£¡£
ICCV 2021 _ µ±Í¼¾í»ýÓöÉ϶àÊÓ½Ç3DÈËÌå×Ë̬Ԥ¼Æ
Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images
Size Wu1,3 Sheng Jin2,3 Wentao Liu3 Lei Bai4 Chen Qian3 Dong Liu1 Wanli Ouyang4 1University of Science and Technology of China 2The University of Hong Kong 3SenseTime Research and Tetras.AI 4The University of Sydney
Wsz32741010@mail.ustc.edu.cn {jinsheng, liuwentao, qianchen}@sensetime.com baisanshi@gmail.com dongeliu@ustc.edu.cn wanly.ouyang@uni.sydney.edu.au
Part 1 ÄîÍ·ºÍÅä¾°

ͼ 1 ±¾ÎÄËã·¨µÄ¿ÉÊÓ»¯Õ¹Ê¾
¶àÊӽǵĶàÈË3D×Ë̬Ԥ¼Æ£¬£¬£¬£¬£¬£¬Ö¸´Ó¶à¸öÏà»ú£¨Òѱ궨£©ÅÄÉãµÄͼÏñÖУ¬£¬£¬£¬£¬£¬»Ö¸´³ö¸Ã³¡¾°µÄËùÓеÄÈËÌå¹Ç¼Ü¡£¡£¡£¡£¡£¡£¡£¡£ËüÊÇÊÓÆµÐж¯ÆÊÎöºÍ¸ß¼¶ÈË»ú½»»¥µÄ»ù´¡£¡£¡£¡£¡£¡£¡£¡£¬£¬£¬£¬£¬£¬ÔÚÔ˶¯ÆÊÎö¡¢Ó°ÊÓÌØÐ§µÈÁìÓò¾ßÓÐÖ÷ÒªµÄÓ¦ÓÃÔ¶¾°¡£¡£¡£¡£¡£¡£¡£¡£
ÏÖÔÚ¶àÊÓ½Ç3DÈËÌå×Ë̬Ԥ¼ÆÓÐÈçÏÂÁ½ÖÖÖ÷Á÷¼Æ»®£º

ͼ 2 Æ¥Åä+Èý½Ç»¯ÖØÐÞËã·¨µÄʾÒâͼ
(a) Æ¥Åä+Èý½Ç»¯ÖØÐÞ£¨Triangulation£©£ºÊ×ÏÈ£¬£¬£¬£¬£¬£¬¼ì²â¸÷¸öÊÓ½ÇµÄ 2D ÈËÌå¹Ç¼Ü£»£»£»£»£»£»£»ÔÙʹÓü¸ºÎ¹ØÏµÅÌËã¾àÀ룬£¬£¬£¬£¬£¬¾ÙÐжàÊÓ½ÇÈËÌåµÄÆ¥Å䣻£»£»£»£»£»£»×îÖÕ£¬£¬£¬£¬£¬£¬Ê¹ÓöàÊӽǵÄ2D×ø±êºÍÏà»ú²ÎÊý£¬£¬£¬£¬£¬£¬ÅÌËã³ö3D×ø±ê¡£¡£¡£¡£¡£¡£¡£¡£
´ú±íÒªÁ죺MVPose[1]
Èõµã£º£¨1£©ÒªÁìµÄ¾«¶ÈÊ®·ÖÒÀÀµ 2D ¼ì²âЧ¹û¡£¡£¡£¡£¡£¡£¡£¡£2D ¼ì²âµÄÎó²î»áÓ°ÏìÆ¥ÅäµÄЧ¹û£¬£¬£¬£¬£¬£¬¹ýʧµÄÆ¥Åä½øÒ»²½µ¼ÖÂÒì³£µÄÖØÐÞ£»£»£»£»£»£»£»2D µÄÎó²îÒ²»áÓ°Ïì 3D ÖØÐ޵ľ«¶È¡£¡£¡£¡£¡£¡£¡£¡££¨2£©Æ¥Å䲿·ÖºÍÈý½ÇÖØÐÞ²¿·Ö²¢²»ÊÇÊý¾ÝÇý¶¯µÄ£¬£¬£¬£¬£¬£¬Ã»ÓмàÊÓѵÁ·ºÍËðʧ·´´«¡£¡£¡£¡£¡£¡£¡£¡£

ͼ 3 3D¿Õ¼äÌåËØ»¯Ëã·¨µÄʾÒâͼ
(b) 3D¿Õ¼äÌåËØ»¯£º½« 3D ¿Õ¼äµÈ¾àµØ»®·ÖΪһ¸ö¸öÐ¡Íø¸ñ£¬£¬£¬£¬£¬£¬Í¨¹ý¸ÅÂÊÄ£×Ó»òÕß 3D ¾í»ýÉñ¾ÍøÂ磨CNN£©¼ì²âÒªº¦µã¡£¡£¡£¡£¡£¡£¡£¡£
´ú±íÒªÁ죺VoxelPose[2]
Èõµã£º£¨1£©¿Õ¼äÌåËØ»¯µÄ¾«¶È»áÊܵ½Íø¸ñ¾ÞϸµÄÖÆÔ¼£¬£¬£¬£¬£¬£¬»á±¬·¢Á¿»¯Îó²î£»£»£»£»£»£»£»£¨2£©¿Õ¼äÌåËØ»¯ÔÚÏàͬµÄ¾«¶ÈÏ£¬£¬£¬£¬£¬£¬ÅÌËãÖØÆ¯ºóËæ¿Õ¼ä¾ÞϸÈý´Î·½ÔöÌí£¬£¬£¬£¬£¬£¬ÎÞ·¨Ó¦ÓÃÓڽϴ󳡾°¡£¡£¡£¡£¡£¡£¡£¡£

ͼ 4 ±¾ÎÄËã·¨µÄʾÒâͼ
(c) ±¾ÎÄÒªÁ죺ÎÒÃÇÍŽáÁ½ÕßµÄÓÅÊÆ£¬£¬£¬£¬£¬£¬Ìá³öÁËÒ»ÖÖ»ùÓÚͼ¾í»ýÉñ¾ÍøÂçµÄ£¬£¬£¬£¬£¬£¬×Ô¶¥ÏòÏ£¨Top-down£©µÄÁ½½×¶ÎËã·¨¡£¡£¡£¡£¡£¡£¡£¡£¹þ¹þ(haha)ÌåÓýÕûÌåËã·¨Á÷³Ì£¬£¬£¬£¬£¬£¬·ÖΪÁ½¸ö½×¶Î£º3DÈËÌåÖÐÐĵ㶨λ+3DÈËÌå×Ë̬Ԥ¼Æ¡£¡£¡£¡£¡£¡£¡£¡£

ͼ 5 ±¾ÎÄÒªÁìµÄÕûÌå¿ò¼Üͼ
1. Õë¶Ô3DÈËÌåÖÐÐĵ㶨룬£¬£¬£¬£¬£¬ÎÒÃǽÓÄÉÁËproposal-refine "ÓÉ´Öµ½¾«"µÄÕ½ÂÔ¡£¡£¡£¡£¡£¡£¡£¡£Ê×ÏȾÙÐС±´Öɸѡ¡±£¬£¬£¬£¬£¬£¬Õ¹ÍûһϵÁкòÑ¡ÖÐÐĵãλÖã¨MMG£©£»£»£»£»£»£»£»ÔÙÔÚºòÑ¡ÖÐÐĵãÖÜΧ¡°Ï¸Ä塱ËÑË÷£¨CRG£©£¬£¬£¬£¬£¬£¬»ñµÃ׼ȷµÄÈËÌåÖÐÐĵ㶨λЧ¹û¡£¡£¡£¡£¡£¡£¡£¡£
Óŵ㣺£¨1£©ÏÖÓÐÒªÁìÔÚÈں϶àÊӽǵÄÌØÕ÷ʱ£¬£¬£¬£¬£¬£¬Í¨³£Ö±½Ó¼¶Áª»ò³Ø»¯¸÷¸öÊӽǵÄÌØÕ÷ÏòÁ¿£¬£¬£¬£¬£¬£¬ºöÊÓÁËÊÓ½ÇÖ®¼äµÄÏ໥¹ØÏµ¡£¡£¡£¡£¡£¡£¡£¡£ÎÒÃÇʹÓÃͼ¾í»ýÍøÂ磬£¬£¬£¬£¬£¬Äܹ»ÓÐÓÃʹÓÿçÊӽǵÄÌØÕ÷¡£¡£¡£¡£¡£¡£¡£¡££¨2£©¹þ¹þ(haha)ÌåÓý"ÓÉ´Öµ½¾«"Õ½ÂÔ£¬£¬£¬£¬£¬£¬Äܹ»ÓÐÓÃÌáÉýÄ£×ÓµÄÕ¹Íû¾«¶È¡£¡£¡£¡£¡£¡£¡£¡££¨3£©ËÑË÷¿Õ¼ä±»ÏÞÖÆÔÚÁ˺òÑ¡ÖÐÐĵãÖÜΧ£¬£¬£¬£¬£¬£¬ÓëÏÖʵµÄ¿Õ¼ä¾ÞϸÎ޹أ¬£¬£¬£¬£¬£¬´ó´ó½µµÍÁËÔËËãÖØÆ¯ºó¡£¡£¡£¡£¡£¡£¡£¡£
2. Õë¶Ô3DÈËÌå×Ë̬Ԥ¼Æ£¬£¬£¬£¬£¬£¬ÎÒÃÇͬÑù½ÓÄÉ"ÓÉ´Öµ½¾«"µÄÕ½ÂÔ£¬£¬£¬£¬£¬£¬Ê×ÏÈÓÃÄ£×ÓÕ¹Íû³õʼ3DÈËÌå×Ë̬£¬£¬£¬£¬£¬£¬ÔÙÅäºÏʹÓÃÈËÌå¹Ç¼Ü½á¹¹ÐÅÏ¢ºÍ¶àÊÓ½ÇÌØÕ÷£¬£¬£¬£¬£¬£¬À´ÓÅ»¯Õ¹ÍûµÄЧ¹û£¨PRG£©¡£¡£¡£¡£¡£¡£¡£¡£
Óŵ㣺ÎÒÃǽÓÄÉÁË"ÓÉ´Öµ½¾«"Õ½ÂÔ¡£¡£¡£¡£¡£¡£¡£¡£Í¼¾í»ýÉñ¾ÍøÂçPRG£¬£¬£¬£¬£¬£¬Äܹ»Ö±½Ó¶ÔÈËÌå¹Ç¼Ü½á¹¹¾ÙÐн¨Ä££¬£¬£¬£¬£¬£¬Ê¹ÓùǼÜÔ¼Êø£¬£¬£¬£¬£¬£¬ÓÅ»¯ÁËÄ£×ÓµÄÕ¹ÍûЧ¹û¡£¡£¡£¡£¡£¡£¡£¡£
Part 2: ÒªÁì
2.1 ÖÐÐĵãºòÑ¡ÇøÓòÌìÉú£ºMulti-view Matching Graph(MMG)
ÎÒÃÇÉè¼ÆÁ˶àÊÓ½ÇÆ¥ÅäͼÉñ¾ÍøÂ磨MMG£©£¬£¬£¬£¬£¬£¬Åжϲî±ðÊӽǵÄÁ½Á½2DÖÐÐĵ㣬£¬£¬£¬£¬£¬ÊÇ·ñÊôÓÚͳһСÎÒ˽¼Ò¡£¡£¡£¡£¡£¡£¡£¡£Ëæºó£¬£¬£¬£¬£¬£¬¹ØÓÚÊôÓÚͳһСÎÒ˽¼ÒµÄÒ»¶ÔÒªº¦µã£¬£¬£¬£¬£¬£¬Á½Á½ÖØÐÞ³öÒ»¸ö3D×ø±ê×÷ΪºòÑ¡ÖÐÐĵ㡣¡£¡£¡£¡£¡£¡£¡£

ͼ 6 MMGÄ£¿£¿£¿£¿£¿£¿£¿éʾÒâͼ
½¨Í¼£¨Graph Construction£©£ºÎÒÃÇʹÓø÷¸öÊӽǼì²â»ñµÃµÄÈËÌå2DÖÐÐĵ㣬£¬£¬£¬£¬£¬À´½á¹¹¿çÊÓ½Çͼģ×Ó¡£¡£¡£¡£¡£¡£¡£¡£Í¼Ä£×ӵġ°½Úµã¡±Îª ¸÷¸öÊӽǼì²âµ½µÄ2DÈËÌåÖÐÐĵ㣻£»£»£»£»£»£»½ÚµãµÄÌØÕ÷Ϊ£º2DÈËÌåÖÐÐĵãλÖõÄͼÏñÌØÕ÷¡£¡£¡£¡£¡£¡£¡£¡£Í¼Ä£×ӵġ°±ß¡±£¬£¬£¬£¬£¬£¬Á½Á½ÅþÁ¬²î±ðÊӽǵĽڵ㣬£¬£¬£¬£¬£¬Á½¸ö½Úµã¶ÔÓ¦µÄ2DÖÐÐĵãµÄ¼«Ïß¾àÀë×÷Ϊ±ßÌØÕ÷¡£¡£¡£¡£¡£¡£¡£¡£
ÐÅϢת´ï£¨Message Passing£©£ºÊ¹ÓÃGNNÀ´¾ÙÐÐÏà¹ØÐÔÌØÕ÷µÄѧϰ¡£¡£¡£¡£¡£¡£¡£¡£ÎÒÃÇʹÓÃEdgeConvÀ´´î½¨Í¼¾í»ýÉñ¾ÍøÂçÄ£×Ó£¬£¬£¬£¬£¬£¬¶ÔËù½á¹¹ºÃµÄGraph ¾ÙÐоí»ý£¬£¬£¬£¬£¬£¬Ò»Ö±¸üнڵãµÄÌØÕ÷£»£»£»£»£»£»£»Í¨¹ýͼµÄ±íÕ÷£¬£¬£¬£¬£¬£¬Ä£×ÓͬʱʹÓÃÁ˼¸ºÎÐÅÏ¢£¨±ßÌØÕ÷£©ºÍͼÏñÐÅÏ¢£¨½ÚµãÌØÕ÷£©£¬£¬£¬£¬£¬£¬¸ßЧµÄÈں϶àÊÓ½ÇÌØÕ÷£¬£¬£¬£¬£¬£¬Æ¥Å侫¶ÈÔ¶¸ßÓÚÖ±½ÓʹÓü«Ï߯¥Åä¡£¡£¡£¡£¡£¡£¡£¡£
ÅжϱߵÄÊôÐÔ£ºÑµÁ·Ò»¸ö±ßÅбðÆ÷£¨Edge Discriminator£©£¬£¬£¬£¬£¬£¬¶Ôÿһ¶ÔÖÐÐĵ㣨¼´Ò»¸ö±ß£©¾ÙÐÐÅб𣬣¬£¬£¬£¬£¬ÅжÏÕâÒ»¶ÔÖÐÐĵãÊÇ·ñÊôÓÚͳһСÎÒ˽¼Ò¡£¡£¡£¡£¡£¡£¡£¡£
Ìá³öºòÑ¡µã£ºÃ¿Ò»¶Ô±»ÅжÏΪͳһСÎÒ˽¼ÒµÄÖÐÐĵ㣬£¬£¬£¬£¬£¬Í¨¹ýÈý½Ç»¯ÖØÐÞ³öÒ»¸ö3DºòÑ¡µã¡£¡£¡£¡£¡£¡£¡£¡£
2. 2 ÖÐÐĵã×ø±êÓÅ»¯£ºCenter Refinement Graph(CRG)
ÓÐÁ˺òÑ¡ÖÐÐĵãºó£¬£¬£¬£¬£¬£¬ÎÒÃÇÒÔºòÑ¡µãΪÇòÐĵÄÇòÐιæÄ£×÷ΪËÑË÷¿Õ¼ä£¬£¬£¬£¬£¬£¬ÎÞаµØÔÚºòÑ¡ÇøÓò²ÉÑù¡£¡£¡£¡£¡£¡£¡£¡£¹ØÓÚÿ¸ö²ÉÑùµã£¬£¬£¬£¬£¬£¬½«²ÉÑùµãͶӰµ½¸÷¸öÊӽDz¢ÔÚÏìӦλÖÃÌáÈ¡ÌØÕ÷¡£¡£¡£¡£¡£¡£¡£¡£½Ó×ÅʹÓÃÖÐÐĵãÓÅ»¯Í¼Ä£×Ó£¨CRG£©£¬£¬£¬£¬£¬£¬Í¨¹ý¶à¸öÊӽǽڵãµÄÅþÁ¬£¬£¬£¬£¬£¬£¬ÊµÏÖÁ˸ßЧµÄ¶àÊÓ½ÇÌØÕ÷Èںϣ¬£¬£¬£¬£¬£¬¿ÉÒÔ׼ȷµØÅжϲÉÑùµãÊÇ·ñΪÈËÌåÖÐÐĵ㡣¡£¡£¡£¡£¡£¡£¡£

ͼ7 CRGÄ£¿£¿£¿£¿£¿£¿£¿éʾÒâͼ
ËÑË÷¿Õ¼ä£ºÒÔºòÑ¡µãΪÇòÐĵÄÇòÐιæÄ£×÷ΪËÑË÷¿Õ¼ä£¬£¬£¬£¬£¬£¬ËùÓÐËÑË÷¿Õ¼äµÄ²¢¼¯×÷Ϊ×ܵÄËÑË÷¿Õ¼ä¡£¡£¡£¡£¡£¡£¡£¡£
²ÉÑù£ºÎÒÃÇ¿ÉÒÔ¾ÙÐÐ˳ӦÐԵIJÉÑù£¬£¬£¬£¬£¬£¬ÏÈÔÚËÑË÷¿Õ¼äÖеȾà²ÉÑù£¬£¬£¬£¬£¬£¬ÓÃÖÐÐĵãͼ¾í»ýÍøÂç¼ì²âÖÐÐĵ㣬£¬£¬£¬£¬£¬ÔÚ¼ì²âµ½µÄÖÐÐĵãÖÜΧ½øÒ»²½Ï¸Äå²ÉÑù£¬£¬£¬£¬£¬£¬ÒÔ»ñµÃ¸ü׼ȷµÄλÖᣡ£¡£¡£¡£¡£¡£¡£
½¨Í¼£¨Graph Construction£©£º¶Ôÿһ¸ö3D ²ÉÑùµã£¬£¬£¬£¬£¬£¬¹¹½¨Ò»¸öͼģ×Ó¡£¡£¡£¡£¡£¡£¡£¡£ÆäÖУ¬£¬£¬£¬£¬£¬½ÚµãºÍ½ÚµãÌØÕ÷»®·Ö¶ÔÓ¦ 3D²ÉÑùµãͶӰµ½¸÷¸öÊӽǺóµÄ2DλÖà ÒÔ¼° ¸ÃλÖõÄͼÏñÌØÕ÷¡£¡£¡£¡£¡£¡£¡£¡£Í¼Öеıߣ¬£¬£¬£¬£¬£¬¶Ô¸÷¸ö½Úµã¾ÙÐÐÈ«ÅþÁ¬¡£¡£¡£¡£¡£¡£¡£¡£
ÐÅϢת´ï£¨Message Passing£©£ºÊ¹ÓÃGCNÀ´¾ÙÐÐÏà¹ØÐÔÌØÕ÷µÄѧϰ¡£¡£¡£¡£¡£¡£¡£¡£ÎÒÃÇʹÓÃEdgeConvÀ´´î½¨Í¼¾í»ýÉñ¾ÍøÂçÄ£×Ó£¬£¬£¬£¬£¬£¬Ê¹Óöà²ãͼ¾í»ýÒ»Ö±¸üнڵãµÄÌØÕ÷¡£¡£¡£¡£¡£¡£¡£¡£
ÅжÏͼµÄÊôÐÔ£ºÊ×ÏȶÔͼÖнڵã¾ÙÐÐÈ«¾Ö³Ø»¯£¬£¬£¬£¬£¬£¬»ñµÃͼµÄÌØÕ÷£¬£¬£¬£¬£¬£¬ÔÙѵÁ·Ò»¸ö¶à²ã¸ÐÖª»úMLPs£¬£¬£¬£¬£¬£¬ÅжÏͼµÄÊôÐÔ£ºÅжϲÉÑùµãÊÇ·ñΪÈËÌåÖÐÐÄ£¬£¬£¬£¬£¬£¬¼´Êä³öÿ¸ö²ÉÑùµãΪÈËÌåÖÐÐĵÄÖÃÐŶȡ£¡£¡£¡£¡£¡£¡£¡£
·Ç¼«´óÖµÒÖÖÆ (NMS)£º»ñµÃÿ¸ö²ÉÑùµãµÄÖÃÐŶȺ󣬣¬£¬£¬£¬£¬Í¨¹ýNMS²Ù×÷£¬£¬£¬£¬£¬£¬»ñµÃÓÅ»¯ºóµÄÈËÌåÖÐÐĵã×ø±ê¡£¡£¡£¡£¡£¡£¡£¡£
2. 3 ÈËÌå×Ë̬ÓÅ»¯£ºPose Regression Graph(PRG)

ͼ8 PRGÄ£¿£¿£¿£¿£¿£¿£¿éʾÒâͼ
ÎÒÃǽÓÄÉ"ÓÉ´Öµ½¾«"µÄÕ½ÂÔ£¬£¬£¬£¬£¬£¬À´Ô¤¼ÆÈËÌå3D×Ë̬¡£¡£¡£¡£¡£¡£¡£¡£Ê×ÏÈ£¬£¬£¬£¬£¬£¬ÎÒÃǽÓÄÉÏÖÓеÄ×Ë̬Ԥ¼ÆÒªÁì[2]£¬£¬£¬£¬£¬£¬»ñµÃ³õʼµÄ3DÈËÌå×Ë̬£¨Initial 3D Pose£©£»£»£»£»£»£»£»ÎªÁËÌáÉýÈËÌåÒªº¦µãµÄÕ¹Íû¾«¶È£¬£¬£¬£¬£¬£¬±¾ÎÄÌá³öÁËÈËÌå×Ë̬»Ø¹éͼģ×Ó£¨Pose Regression Graph, PRG£©£¬£¬£¬£¬£¬£¬Ê¹ÓÃͼ¾í»ý£¬£¬£¬£¬£¬£¬¸ßЧµØÈں϶àÊӽǵÄÌØÕ÷ºÍÈËÌåµÄÍØÆË½á¹¹ÐÅÏ¢£¨Í·ºÍ²±×ÓÏàÁ¬£¬£¬£¬£¬£¬£¬Êֺ͸첲ÏàÁ¬µÈ£©£¬£¬£¬£¬£¬£¬»Ø¹é³öÿ¸öÒªº¦µã×ø±êµÄÐÞÕýÖµ¡£¡£¡£¡£¡£¡£¡£¡£
½¨Í¼£¨Graph Construction£©£ºÊ×ÏȽ«³õʼ3DÈËÌå×Ë̬£¬£¬£¬£¬£¬£¬Í¶Ó°µ½¸÷¸öÏà»úÊӽǣ¬£¬£¬£¬£¬£¬»ñµÃ¸÷¸öÊӽǵÄ2DÈËÌå×Ë̬¡£¡£¡£¡£¡£¡£¡£¡£Ê¹Óø÷¸öÊÓ½ÇϵÄ2DÈËÌåÒªº¦µã£¬£¬£¬£¬£¬£¬½á¹¹¿çÊÓ½Ç×Ë̬ͼ£¨Multi-view Pose Graph£©¡£¡£¡£¡£¡£¡£¡£¡£
ͼģ×ÓµÄÿ¸ö¡°½Úµã¡±´ú±í¸÷¸öÊӽǵÄÿ¸ö2DÒªº¦µã¡£¡£¡£¡£¡£¡£¡£¡£½ÚµãµÄÌØÕ÷°üÀ¨£ºÒªº¦µãµÄÖÖ±ðÐÅÏ¢£¬£¬£¬£¬£¬£¬2DÒªº¦µãλÖô¦µÄͼÏñÌØÕ÷£¬£¬£¬£¬£¬£¬ÒÔ¼°³õʼ3DÈËÌå×Ë̬ÖжÔÓ¦Òªº¦µãµÄÖÃÐŶȡ£¡£¡£¡£¡£¡£¡£¡£
ͼģ×ӵġ°±ß¡±´ú±í½ÚµãÖ®¼äµÄ¹ØÏµ£¬£¬£¬£¬£¬£¬¹²°üÀ¨Á½ÖÖÀàÐ͵ıߣº¿çÊÓ½ÇÇÒÏàͬÀàÐÍÒªº¦µã¼äµÄÅþÁ¬±ß ºÍ µ¥ÊÓ½ÇÇÒ²î±ðÀàÐÍÒªº¦µã¼äµÄÅþÁ¬±ß¡£¡£¡£¡£¡£¡£¡£¡£
ÐÅϢת´ï£¨Message Passing£©£ºÊ¹ÓÃGCNÀ´¾ÙÐÐÏà¹ØÐÔÌØÕ÷µÄѧϰ¡£¡£¡£¡£¡£¡£¡£¡£ÎÒÃÇʹÓÃEdgeConvÀ´´î½¨Í¼¾í»ýÉñ¾ÍøÂçÄ£×Ó£¬£¬£¬£¬£¬£¬¶ÔËù½á¹¹ºÃµÄGraph ¾ÙÐоí»ý£¬£¬£¬£¬£¬£¬Ò»Ö±¸üнڵãµÄÌØÕ÷£»£»£»£»£»£»£»Íê³É¶àÊÓ½ÇÌØÕ÷µÄ¸üкÍÈںϺ󣬣¬£¬£¬£¬£¬¶ÔÏàͬÀàÐ͵ÄÒªº¦µãÌØÕ÷¾ÙÐÐ×î´óÖµ³Ø»¯£¬£¬£¬£¬£¬£¬»ñµÃÒ»¸±ÈËÌå¹Ç¼Ü¡£¡£¡£¡£¡£¡£¡£¡£
»Ø¹éÐÞÕýÖµ£¨Regression£©£ºÊ¹ÓûعéÄ£×Ó£¬£¬£¬£¬£¬£¬Õ¹Íû³ö Nx3 άµÄÆ«ÒÆÏòÁ¿£¨ÆäÖУ¬£¬£¬£¬£¬£¬N´ú±íÒªº¦µã¸öÊý£©£¬£¬£¬£¬£¬£¬´ú±íÏà¹ØÓÚ³õʼ3DÈËÌå×Ë̬µÄÐÞÕýÖµ¡£¡£¡£¡£¡£¡£¡£¡£
Part 3: ʵÑéЧ¹û
ÎÒÃÇÔÚ CMU Panoptic ºÍ Shelf Á½¸öÖ÷Á÷µÄÊý¾Ý¼¯ÉÏ×öÁËʵÑé¡£¡£¡£¡£¡£¡£¡£¡£¶¨Á¿ÊµÑéÅú×¢£¬£¬£¬£¬£¬£¬ÎÒÃÇÌá³öµÄËã·¨£¬£¬£¬£¬£¬£¬È¡µÃÁË×îÓŵľ«¶È¡£¡£¡£¡£¡£¡£¡£¡£²¢ÇÒÔÚÅÌËãÁ¿ºÍºÄʱ·½Ã棬£¬£¬£¬£¬£¬¹þ¹þ(haha)ÌåÓýÒªÁìÏà±È֮ǰµÄSOTAÒ²ÓÐÏÔ×ÅÓÅÊÆ¡£¡£¡£¡£¡£¡£¡£¡£

±í1 CMU Panoptic Êý¾Ý¼¯µÄ¶¨Á¿Ð§¹û±ÈÕÕ

±í2 Shelf Êý¾Ý¼¯µÄ¶¨Á¿Ð§¹û±ÈÕÕ
Part 4: ×ܽáÓëÕ¹Íû
ÔÚ±¾ÎÄÖУ¬£¬£¬£¬£¬£¬ÎÒÃÇÌá³öÁËÒ»Ì××Ô¶¥ÏòϵĶàÊÓ½Ç3DÈËÌå×Ë̬Ԥ¼Æ½â¾ö¼Æ»®¡£¡£¡£¡£¡£¡£¡£¡£ÎÒÃÇÕë¶Ô¸ÃʹÃü£¬£¬£¬£¬£¬£¬È«ÐÄÉè¼ÆÁËÖÖÖÖ¡°¶àÊӽǡ±Í¼¾í»ýÉñ¾ÍøÂ磨MMG, CRG, PRG£©À´ÌáÈ¡ÈËÌå½á¹¹ÐÔÌØÕ÷¡£¡£¡£¡£¡£¡£¡£¡£ÎÒÃÇÔÚ¸÷Êý¾Ý¼¯ÉϵÄʵÑ飬£¬£¬£¬£¬£¬Ò²³ä·Ö֤ʵÎúÎÒÃÇËã·¨µÄÓÐÓÃÐÔ¡£¡£¡£¡£¡£¡£¡£¡£¹ØÓÚ¶ÔδÀ´µÄÕ¹Íû£¬£¬£¬£¬£¬£¬ÎÒÃǽ«¼ÌÐøÑо¿°ÑËã·¨À©Õ¹µ½Ê±Ðò£¬£¬£¬£¬£¬£¬ÊµÏÖ¸ü¸ßЧµÄ¶àÊÓ½ÇÈËÌå×Ë̬¸ú×Ù¡£¡£¡£¡£¡£¡£¡£¡£ÔÚÒªÁì²ãÃæ£¬£¬£¬£¬£¬£¬ÔõÑùÔ½·¢ºÏÀíµØÊ¹ÓÃÏà»úµÄ¼¸ºÎÐÅÏ¢£¬£¬£¬£¬£¬£¬Éè¼Æ¸ü¸ßЧµÄͼ¾í»ýÉñ¾ÍøÂ磬£¬£¬£¬£¬£¬ÊÇÒ»¸öÖ÷ÒªµÄË¢ÐÂÆ«Ïò¡£¡£¡£¡£¡£¡£¡£¡£
Reference:
[1] Dong, J., Jiang, W., Huang, Q., Bao, H., & Zhou, X. (2019). Fast and robust multi-person 3d pose estimation from multiple views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7792-7801).
[2] Tu, H., Wang, C., & Zeng, W. (2020). Voxelpose: Towards multi-camera 3d human pose estimation in wild environment. In Computer Vision¨CECCV 2020: 16th European Conference, Glasgow, UK, August 23¨C28, 2020, Proceedings, Part I 16 (pp. 197-212). Springer International Publishing.





·µ»Ø