| LDR | | 01573nam u200373 4500 |
| 001 | | 000000418520 |
| 005 | | 20190215162930 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438325609 |
| 035 | |
▼a (MiAaPQ)AAI10843062 |
| 035 | |
▼a (MiAaPQ)berkeley:18085 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Tulsiani, Shubham. |
| 245 | 10 |
▼a Learning Single-view 3D Reconstruction of Objects and Scenes. |
| 260 | |
▼a [S.l.]:
▼b University of California, Berkeley.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 123 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Jitendra Malik. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a We address the task of inferring the 3D structure underlying an image, in particular focusing on two questions -- how we can plausibly obtain supervisory signal for this task, and what forms of representation should we pursue. We first show that |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999891
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |