来自Blender的PNG纹理文件而不是mtl文件

时间:2017-09-21 05:42:43

标签: export blender render-to-texture arcore

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。

1 个答案:

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)

enter image description here

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)”

enter image description here

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%