Comments (1)
The encoder is resnet18 which is specialized for input image of 224 * 224. You cannot change the input size to 448 unless you modify the encoder. Nevertheless, I think increase the size of input image can barely improve the reconstruction results. The structure of onet uses no local features. It only uses global features which should be consistent to image size.
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Related Issues (20)
- Can This Method Generate Color, Texture and Coordinate Information?
- ModuleNotFoundError: No module named 'im2mesh.utils.libvoxelize.voxelize' HOT 3
- Download dataset HOT 1
- setup.py fails: clang: error: unsupported option '-fopenmp' ninja: build stopped: subcommand failed. HOT 1
- undefined symbol: cblas_sgemm_alloc HOT 9
- Error: command . . . cl.exe' failed with exit status 2 HOT 1
- Question about points_uniform_ratio
- Compiler error HOT 3
- Question about YAML files
- Error occured when loading field inputs of model HOT 2
- The value of kl divergence is always 0
- Can we remove the version for the dependencies in environment.yaml HOT 5
- How could we get the "p0_z" for the kl loss?
- how to generate the format like pre-processed shapenet?
- Reconstruction loss for variational inference (PointNet case)
- ResolvePackageNotFound: - pytorch=1.0.0 , when I run `conda env create -f environment.yaml` HOT 3
- LINK : fatal error LNK1181: Cannot open input file 'm.lib'.
- My program is running smoothly, but the generated.off file is empty HOT 8
- How to use the trained model to convert the point cloud into mesh?
- compiler error: AttributeError: module 'pkgutil' has no attribute 'ImpImporter'. Did you mean: 'zipimporter'?
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