Comments (4)
你的深度图是正常的吗?训练的时候深度图正常,测试不正常或者如果深度图是正常的,融合的点云是错误的可能是相机参数的问题(相机参数和图像尺寸不匹配也可能),或者点云是正常的但非常稀疏那可能是点云过滤阈值过大,比如光度一致性和几何一致性。
from unimvsnet.
你的深度图是正常的吗?训练的时候深度图正常,测试不正常或者如果深度图是正常的,融合的点云是错误的可能是相机参数的问题(相机参数和图像尺寸不匹配也可能),或者点云是正常的但非常稀疏那可能是点云过滤阈值过大,比如光度一致性和几何一致性。
谢谢你的答复,这是我刚刚跑的,用的是BlendedMVS里,BlendedMVS/dataset_low_res/58cf4771d0f5fb221defe6da这个场景,只有64张照片,训练一开始Th2、Th4、Th8输出都是0,预测出来没有点云
双gpu,batchsize=1
输出是这样的:
Epoch 0/16: 100% 32/32 || Elapsed Time: 0:01:02,Time: 0:01:02,LR: 0.001,Loss: 0.223|0.414,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.253|0.248,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 1/16: 100% 32/32 || Elapsed Time: 0:00:44,Time: 0:00:44,LR: 0.001,Loss: 0.282|0.242,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.297|0.286,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 2/16: 100% 32/32 || Elapsed Time: 0:00:42,Time: 0:00:42,LR: 0.001,Loss: 0.335|0.324,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:08,Time: 0:00:08,Loss: 0.362|0.349,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 3/16: 15% 05/32 || Elapsed Time: 0:00:09,ETA: 0:00:51,LR: 0.001,Loss: 0.352|0.350,Th2Epoch 3/16: 18% 06/32 || Elapsed Time: 0:00:10,ETA: 0:00:47,LR: 0.001,Loss: 0.356|0.351,Th2Epoch 3/16: 21% 07/32 || Elapsed Time: 0:0EpEpoch 3/16: 100% 32/32 || Elapsed Time: 0:00:43,Time: 0:00:43,LR: 0.001,Loss: 0.340|0.353,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000,Loss: 0.350|0.352,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.345|0.351,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 4/16: 100% 32/32 || Elapsed Time: 0:00:43,Time: 0:00:43,LR: 0.001,Loss: 0.334|0.341,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.310|0.316,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 5/16: 100% 32/32 || Elapsed Time: 0:00:42,Time: 0:00:42,LR: 0.001,Loss: 0.304|0.321,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:08,Time: 0:00:08,Loss: 0.302|0.310,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 6/16: 100% 32/32 || Elapsed Time: 0:00:43,Time: 0:00:43,LR: 0.001,Loss: 0.289|0.302,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.287|0.294,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 7/16: 100% 32/32 || Elapsed Time: 0:00:43,Time: 0:00:43,LR: 0.001,Loss: 0.294|0.287,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.272|0.281,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 8/16: 100% 32/32 || Elapsed Time: 0:00:42,Time: 0:00:42,LR: 0.001,Loss: 0.295|0.280,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:08,Time: 0:00:08,Loss: 0.269|0.275,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 9/16: 100% 32/32 || Elapsed Time: 0:00:43,Time: 0:00:43,LR: 0.001,Loss: 0.271|0.275,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.265|0.275,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 10/16: 100% 32/32 || Elapsed Time: 0:00:43,Time: 0:00:43,LR: 0.0005,Loss: 0.267|0.272,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Val:100% 32/32 || Elapsed Time: 0:00:09,Time: 0:00:09,Loss: 0.263|0.272,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.000
Epoch 11/16: 43% 14/32 || Elapsed Time: 0:00:20,ETA: 0:00:26,LR: 0.0005,Loss: 0.260|0.270,Th2: 0.000|0.000,Th4: 0.000|0.000,Th8: 0.000|0.0
from unimvsnet.
可能你的dataset加载训练样本有问题
from unimvsnet.
可能你的dataset加载训练样本有问题
谢谢,已经解决了,自己的数据是训练时间不够
from unimvsnet.
Related Issues (20)
- can not download DTU, Tank and Temples Dataset HOT 1
- Some questions about the differences between UniMVS and CasMVS HOT 2
- Different results between released model and released ply HOT 3
- About abs_depthloss HOT 3
- 请问这个训练和测试时的源图像数量保持一致对结果有影响吗? HOT 2
- empty output point cloud HOT 9
- Question about gipuma HOT 3
- Got OOM error when calculating the gt_unity_index_volume HOT 1
- The evaluation results on DTU evaluation set are different between paper and released checkpoints HOT 2
- A problem about fusibile? HOT 1
- Error: no kernel image is available for execution on the device HOT 4
- About unity generation HOT 1
- 关于坦克和寺庙数据集的调参问题! HOT 2
- About your device HOT 1
- 测试自己的数据集
- custom data test reconstruction effect is not good HOT 1
- fusibile中的问题 HOT 1
- About the setting of the 3-step conf values in a new dataset HOT 1
- 关于训练dtu数据时第七个epoch验证时报错
- Why is it that after running the test, the point cloud is divergent and the depth map is also blurry HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from unimvsnet.