我正在尝试Google的ARCore。但是我无法将我在Blender中的3D模型导出到obj和png组合而不是默认的obj和mtl组合。我对Blender有点新意。我也尝试过来自Stack Exchange的this,但没有用。
答案 0 :(得分:0)
如果您尝试使用ARCore,如果您的重点是Sceneform,这可能是一个解决方案。
导出.obj和.mtl后,使用示例数据目录在Android中创建一个文件夹(以避免将其纳入您的项目)
在此文件夹中添加.obj和.mtl
转到Android 首选项> 插件,然后搜索“ Google Sceneform Tools(Beta)”
在.obj上单击鼠标右键,可以选择“导入Sceneform资产” 这将创建已经准备使用的.sfb和.sfa文件。
在您的 App Gradle 中,您应该找到类似以下的新行:
0.0000554776 => 3868b0a6 => 0e8b => 0.0000554770 | error: 0.001%
0.0000499299 => 38516bc8 => 0d17 => 0.0000499338 | error: 0.008%
0.0000449369 => 383c7a9a => 0bc8 => 0.0000449419 | error: 0.011%
0.0000404432 => 3829a18a => 0a9a => 0.0000404418 | error: 0.004%
0.0000363989 => 3818aafc => 098b => 0.0000364035 | error: 0.013%
0.0000327590 => 380966af => 0896 => 0.0000327528 | error: 0.019%
0.0000294831 => 37f7526e => 07bb => 0.0000294894 | error: 0.021%
0.0000265348 => 37de96fc => 06f5 => 0.0000265390 | error: 0.016%
0.0000238813 => 37c854af => 0643 => 0.0000238866 | error: 0.022%
0.0000214932 => 37b44c37 => 05a2 => 0.0000214875 | error: 0.026%
0.0000193438 => 37a24498 => 0512 => 0.0000193417 | error: 0.011%
0.0000174095 => 37920a89 => 0490 => 0.0000174046 | error: 0.028%
0.0000156685 => 37836fe1 => 041b => 0.0000156611 | error: 0.047%
0.0000141017 => 376c962e => 03b2 => 0.0000140965 | error: 0.037%
0.0000126915 => 3754ed8f => 0354 => 0.0000126958 | error: 0.034%
0.0000114223 => 373fa29a => 02ff => 0.0000114292 | error: 0.060%
0.0000102801 => 372c78be => 02b2 => 0.0000102818 | error: 0.016%
0.0000092521 => 371b3978 => 026d => 0.0000092536 | error: 0.016%
0.0000083269 => 370bb3b9 => 022f => 0.0000083297 | error: 0.034%
0.0000074942 => 36fb76b3 => 01f7 => 0.0000074953 | error: 0.014%
0.0000067448 => 36e2513a => 01c5 => 0.0000067502 | error: 0.081%
0.0000060703 => 36cbaf81 => 0197 => 0.0000060648 | error: 0.091%
0.0000054633 => 36b75127 => 016f => 0.0000054687 | error: 0.100%
0.0000049169 => 36a4fc3c => 014a => 0.0000049174 | error: 0.009%
0.0000044253 => 36947c9c => 0129 => 0.0000044256 | error: 0.009%
0.0000039827 => 3685a359 => 010b => 0.0000039786 | error: 0.103%
0.0000035845 => 36708c6d => 00f1 => 0.0000035912 | error: 0.188%
0.0000032260 => 36587e62 => 00d8 => 0.0000032187 | error: 0.228%
0.0000029034 => 3642d825 => 00c3 => 0.0000029057 | error: 0.080%
0.0000026131 => 362f5c21 => 00af => 0.0000026077 | error: 0.205%
0.0000023518 => 361dd2ea => 009e => 0.0000023544 | error: 0.112%
0.0000021166 => 360e0a9f => 008e => 0.0000021160 | error: 0.029%
0.0000019049 => 35ffacb7 => 0080 => 0.0000019073 | error: 0.127%
0.0000017144 => 35e61b71 => 0073 => 0.0000017136 | error: 0.047%
0.0000015430 => 35cf18b2 => 0068 => 0.0000015497 | error: 0.436%
0.0000013887 => 35ba6306 => 005d => 0.0000013858 | error: 0.208%
0.0000012498 => 35a7bf85 => 0054 => 0.0000012517 | error: 0.150%
0.0000011248 => 3596f92b => 004b => 0.0000011176 | error: 0.645%
0.0000010124 => 3587e040 => 0044 => 0.0000010133 | error: 0.091%
0.0000009111 => 357493a6 => 003d => 0.0000009090 | error: 0.236%
0.0000008200 => 355c1e7b => 0037 => 0.0000008196 | error: 0.054%
0.0000007380 => 35461b6e => 0032 => 0.0000007451 | error: 0.955%
0.0000006642 => 35324be3 => 002d => 0.0000006706 | error: 0.955%
0.0000005978 => 3520777f => 0028 => 0.0000005960 | error: 0.291%
0.0000005380 => 35106b8c => 0024 => 0.0000005364 | error: 0.291%
0.0000004842 => 3501fa64 => 0020 => 0.0000004768 | error: 1.522%
0.0000004358 => 34e9f5e7 => 001d => 0.0000004321 | error: 0.838%
0.0000003922 => 34d29083 => 001a => 0.0000003874 | error: 1.218%
0.0000003530 => 34bd820f => 0018 => 0.0000003576 | error: 1.315%
0.0000003177 => 34aa8ea7 => 0015 => 0.0000003129 | error: 1.499%
0.0000002859 => 34998063 => 0013 => 0.0000002831 | error: 0.978%
0.0000002573 => 348a26bf => 0011 => 0.0000002533 | error: 1.557%
0.0000002316 => 3478ac24 => 0010 => 0.0000002384 | error: 2.947%
0.0000002084 => 345fce20 => 000e => 0.0000002086 | error: 0.087%
0.0000001876 => 34496cb6 => 000d => 0.0000001937 | error: 3.264%
0.0000001688 => 3435483d => 000b => 0.0000001639 | error: 2.914%
0.0000001519 => 3423276a => 000a => 0.0000001490 | error: 1.933%
0.0000001368 => 3412d6ac => 0009 => 0.0000001341 | error: 1.933%
0.0000001231 => 3404279b => 0008 => 0.0000001192 | error: 3.144%
0.0000001108 => 33ede0e3 => 0007 => 0.0000001043 | error: 5.834%
0.0000000997 => 33d61732 => 0007 => 0.0000001043 | error: 4.629%
0.0000000897 => 33c0ae79 => 0006 => 0.0000000894 | error: 0.354%
0.0000000808 => 33ad69d3 => 0005 => 0.0000000745 | error: 7.735%
0.0000000727 => 339c1271 => 0005 => 0.0000000745 | error: 2.517%
0.0000000654 => 338c76ff => 0004 => 0.0000000596 | error: 8.874%
0.0000000589 => 337cd631 => 0004 => 0.0000000596 | error: 1.251%
0.0000000530 => 33638d92 => 0004 => 0.0000000596 | error: 12.501%
0.0000000477 => 334ccc36 => 0003 => 0.0000000447 | error: 6.249%
0.0000000429 => 33385163 => 0003 => 0.0000000447 | error: 4.168%
0.0000000386 => 3325e2d9 => 0003 => 0.0000000447 | error: 15.742%
0.0000000348 => 33154c29 => 0002 => 0.0000000298 | error: 14.265%
0.0000000313 => 33065e25 => 0002 => 0.0000000298 | error: 4.739%
0.0000000282 => 32f1dca9 => 0002 => 0.0000000298 | error: 5.846%
0.0000000253 => 32d9acfe => 0002 => 0.0000000298 | error: 17.606%
0.0000000228 => 32c3e87e => 0002 => 0.0000000298 | error: 30.673%
0.0000000205 => 32b0513e => 0001 => 0.0000000149 | error: 27.404%
0.0000000185 => 329eaf84 => 0001 => 0.0000000149 | error: 19.337%
0.0000000166 => 328ed12a => 0001 => 0.0000000149 | error: 10.375%
0.0000000150 => 3280890c => 0001 => 0.0000000149 | error: 0.416%
0.0000000135 => 32675d15 => 0001 => 0.0000000149 | error: 10.648%
0.0000000121 => 32503a2c => 0001 => 0.0000000149 | error: 22.943%
0.0000000109 => 323b678e => 0001 => 0.0000000149 | error: 36.603%
0.0000000098 => 3228aa00 => 0001 => 0.0000000149 | error: 51.781%
0.0000000088 => 3217cc33 => 0001 => 0.0000000149 | error: 68.646%
0.0000000080 => 32089e2e => 0001 => 0.0000000149 | error: 87.384%
0.0000000072 => 31f5e986 => 0000 => 0.0000000000 | error: 100.000%