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[ICCV 2023] PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs

Home Page: https://manycore-research.github.io/PlankAssembly/

License: GNU Affero General Public License v3.0

Python 100.00%
3d-modeling 3d-reconstruction computer-vision deep-learning engineering-drawing pytorch transformer

plankassembly's Issues

AssertionError

Dear,thank you very much for your work, but I found several issues when I reproduced your code.

Firstly, I run the fourth command (Python dataset/prepare_info. py -- root path/to/data/root), and there is no root parameter in the code.

Then I run the training command (python trainer_complete.py fit --config configs/train_complete.yaml), run 19 epochs and display “AssertionError: expecting key_padding_mask shape of (16, 1157), but got torch.Size([16, 1199])”.

How can I solve this problem?

Reconstructing 3d objects with curves

The current model performs fine with objects that have distinct edges or are composed of square and rectangular shapes. Can it be adapted to process data for images that contain curves, as shown in the SVG and 3D model below?

Additionally, can the bellow SVG be segmented into separate views—front, top, and side—to input into the model?

92552_38c9173a_0000
92552_38c9173a_0000

use SVG files as input

I aim to input SVG files representing the top, bottom, and side orthographic projections of an object. I want to transform these SVG files into a data format suitable for model input data, to reconstruct 3d model

can you give me code reference to do so

系统问题

作者你好。想请问下您是在什么系统下跑的?因为我在训练得那一步出了点问题

Data format

Hello,

I have an additional question regarding the way of preparing the data for inference.

When I run the testing script using the configs/train.yaml, I get the following error:

File "/home/guist/PlankAssembly-atlas/plankassembly/data.py", line 246, in getitem
svgs = info['svgs']
KeyError: 'svgs'

I checked the json file and there is indeed no 'svgs' field so I was wondering how to modify the file so that it works. More generally, how should I prepare my custom images so that I can apply your model on them?

Thanks a lot for the answer.

Best regards,
Guillaume

Unexpected and missing keys

Hello,

I am trying to run the testing script for raster images. I saved the pre-trained checkpoint in the models/ directory and the R-50.pth file in the weights/ directory. Then I typed the command line:

python trainer.py test --config configs/train.yaml --ckpt_path models/line_complete-checkpoint_999-precision=0.944-recall=0.934-f1=0.938.ckpt --trainer.devices 1

But I got the error below. Could you please tell me what went wrong?

Thank you!

Best regards,
Guillaume

raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Trainer:
Missing key(s) in state_dict: "backbone2d.0.fpn_lateral2.weight", "backbone2d.0.fpn_lateral2.norm.weight", "backbone2d.0.fpn_lateral2.norm.bias", "backbone2d.0.fpn_lateral2.norm.running_mean", "backbone2d.0.fpn_lateral2.norm.running_var", "backbone2d.0.fpn_output2.weight", "backbone2d.0.fpn_output2.norm.weight", "backbone2d.0.fpn_output2.norm.bias", "backbone2d.0.fpn_output2.norm.running_mean", "backbone2d.0.fpn_output2.norm.running_var", "backbone2d.0.fpn_lateral3.weight", "backbone2d.0.fpn_lateral3.norm.weight", "backbone2d.0.fpn_lateral3.norm.bias", "backbone2d.0.fpn_lateral3.norm.running_mean", "backbone2d.0.fpn_lateral3.norm.running_var", "backbone2d.0.fpn_output3.weight", "backbone2d.0.fpn_output3.norm.weight", "backbone2d.0.fpn_output3.norm.bias", "backbone2d.0.fpn_output3.norm.running_mean", "backbone2d.0.fpn_output3.norm.running_var", "backbone2d.0.fpn_lateral4.weight", "backbone2d.0.fpn_lateral4.norm.weight", "backbone2d.0.fpn_lateral4.norm.bias", 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"heads3d.decoder.layers.1.self_attn.out_proj.weight", "heads3d.decoder.layers.1.self_attn.out_proj.bias", "heads3d.decoder.layers.1.multihead_attn.in_proj_weight", "heads3d.decoder.layers.1.multihead_attn.in_proj_bias", "heads3d.decoder.layers.1.multihead_attn.out_proj.weight", "heads3d.decoder.layers.1.multihead_attn.out_proj.bias", "heads3d.decoder.layers.1.linear1.weight", "heads3d.decoder.layers.1.linear1.bias", "heads3d.decoder.layers.1.linear2.weight", "heads3d.decoder.layers.1.linear2.bias", "heads3d.decoder.layers.1.norm1.weight", "heads3d.decoder.layers.1.norm1.bias", "heads3d.decoder.layers.1.norm2.weight", "heads3d.decoder.layers.1.norm2.bias", "heads3d.decoder.layers.1.norm3.weight", "heads3d.decoder.layers.1.norm3.bias", "heads3d.decoder.layers.2.self_attn.in_proj_weight", "heads3d.decoder.layers.2.self_attn.in_proj_bias", "heads3d.decoder.layers.2.self_attn.out_proj.weight", "heads3d.decoder.layers.2.self_attn.out_proj.bias", "heads3d.decoder.layers.2.multihead_attn.in_proj_weight", "heads3d.decoder.layers.2.multihead_attn.in_proj_bias", "heads3d.decoder.layers.2.multihead_attn.out_proj.weight", "heads3d.decoder.layers.2.multihead_attn.out_proj.bias", "heads3d.decoder.layers.2.linear1.weight", "heads3d.decoder.layers.2.linear1.bias", "heads3d.decoder.layers.2.linear2.weight", "heads3d.decoder.layers.2.linear2.bias", "heads3d.decoder.layers.2.norm1.weight", "heads3d.decoder.layers.2.norm1.bias", "heads3d.decoder.layers.2.norm2.weight", "heads3d.decoder.layers.2.norm2.bias", "heads3d.decoder.layers.2.norm3.weight", "heads3d.decoder.layers.2.norm3.bias", "heads3d.decoder.layers.3.self_attn.in_proj_weight", "heads3d.decoder.layers.3.self_attn.in_proj_bias", "heads3d.decoder.layers.3.self_attn.out_proj.weight", "heads3d.decoder.layers.3.self_attn.out_proj.bias", "heads3d.decoder.layers.3.multihead_attn.in_proj_weight", "heads3d.decoder.layers.3.multihead_attn.in_proj_bias", "heads3d.decoder.layers.3.multihead_attn.out_proj.weight", "heads3d.decoder.layers.3.multihead_attn.out_proj.bias", "heads3d.decoder.layers.3.linear1.weight", "heads3d.decoder.layers.3.linear1.bias", "heads3d.decoder.layers.3.linear2.weight", "heads3d.decoder.layers.3.linear2.bias", "heads3d.decoder.layers.3.norm1.weight", "heads3d.decoder.layers.3.norm1.bias", "heads3d.decoder.layers.3.norm2.weight", "heads3d.decoder.layers.3.norm2.bias", "heads3d.decoder.layers.3.norm3.weight", "heads3d.decoder.layers.3.norm3.bias", "heads3d.decoder.layers.4.self_attn.in_proj_weight", "heads3d.decoder.layers.4.self_attn.in_proj_bias", "heads3d.decoder.layers.4.self_attn.out_proj.weight", "heads3d.decoder.layers.4.self_attn.out_proj.bias", "heads3d.decoder.layers.4.multihead_attn.in_proj_weight", "heads3d.decoder.layers.4.multihead_attn.in_proj_bias", "heads3d.decoder.layers.4.multihead_attn.out_proj.weight", "heads3d.decoder.layers.4.multihead_attn.out_proj.bias", "heads3d.decoder.layers.4.linear1.weight", "heads3d.decoder.layers.4.linear1.bias", "heads3d.decoder.layers.4.linear2.weight", "heads3d.decoder.layers.4.linear2.bias", "heads3d.decoder.layers.4.norm1.weight", "heads3d.decoder.layers.4.norm1.bias", "heads3d.decoder.layers.4.norm2.weight", "heads3d.decoder.layers.4.norm2.bias", "heads3d.decoder.layers.4.norm3.weight", "heads3d.decoder.layers.4.norm3.bias", "heads3d.decoder.layers.5.self_attn.in_proj_weight", "heads3d.decoder.layers.5.self_attn.in_proj_bias", "heads3d.decoder.layers.5.self_attn.out_proj.weight", "heads3d.decoder.layers.5.self_attn.out_proj.bias", "heads3d.decoder.layers.5.multihead_attn.in_proj_weight", "heads3d.decoder.layers.5.multihead_attn.in_proj_bias", "heads3d.decoder.layers.5.multihead_attn.out_proj.weight", "heads3d.decoder.layers.5.multihead_attn.out_proj.bias", "heads3d.decoder.layers.5.linear1.weight", "heads3d.decoder.layers.5.linear1.bias", "heads3d.decoder.layers.5.linear2.weight", "heads3d.decoder.layers.5.linear2.bias", "heads3d.decoder.layers.5.norm1.weight", "heads3d.decoder.layers.5.norm1.bias", "heads3d.decoder.layers.5.norm2.weight", "heads3d.decoder.layers.5.norm2.bias", "heads3d.decoder.layers.5.norm3.weight", "heads3d.decoder.layers.5.norm3.bias", "heads3d.decoder.norm.weight", "heads3d.decoder.norm.bias", "heads3d.vocab_head.weight", "heads3d.vocab_head.bias", "heads3d.pointer_head.weight", "heads3d.pointer_head.bias", "heads3d.switch_head.weight", "heads3d.switch_head.bias".
Unexpected key(s) in state_dict: "model.input_embeddings.input_value.weight", "model.input_embeddings.input_pos.weight", "model.input_embeddings.input_coord.weight", "model.input_embeddings.input_view.weight", "model.input_embeddings.input_type.weight", "model.query_coord_embedding.weight", "model.query_pos_embedding.weight", "model.encoder.layers.0.self_attn.in_proj_weight", "model.encoder.layers.0.self_attn.in_proj_bias", "model.encoder.layers.0.self_attn.out_proj.weight", "model.encoder.layers.0.self_attn.out_proj.bias", "model.encoder.layers.0.linear1.weight", "model.encoder.layers.0.linear1.bias", "model.encoder.layers.0.linear2.weight", "model.encoder.layers.0.linear2.bias", "model.encoder.layers.0.norm1.weight", "model.encoder.layers.0.norm1.bias", "model.encoder.layers.0.norm2.weight", "model.encoder.layers.0.norm2.bias", "model.encoder.layers.1.self_attn.in_proj_weight", "model.encoder.layers.1.self_attn.in_proj_bias", "model.encoder.layers.1.self_attn.out_proj.weight", "model.encoder.layers.1.self_attn.out_proj.bias", "model.encoder.layers.1.linear1.weight", "model.encoder.layers.1.linear1.bias", "model.encoder.layers.1.linear2.weight", "model.encoder.layers.1.linear2.bias", "model.encoder.layers.1.norm1.weight", "model.encoder.layers.1.norm1.bias", "model.encoder.layers.1.norm2.weight", "model.encoder.layers.1.norm2.bias", "model.encoder.layers.2.self_attn.in_proj_weight", "model.encoder.layers.2.self_attn.in_proj_bias", "model.encoder.layers.2.self_attn.out_proj.weight", "model.encoder.layers.2.self_attn.out_proj.bias", "model.encoder.layers.2.linear1.weight", "model.encoder.layers.2.linear1.bias", "model.encoder.layers.2.linear2.weight", "model.encoder.layers.2.linear2.bias", "model.encoder.layers.2.norm1.weight", "model.encoder.layers.2.norm1.bias", "model.encoder.layers.2.norm2.weight", "model.encoder.layers.2.norm2.bias", "model.encoder.layers.3.self_attn.in_proj_weight", "model.encoder.layers.3.self_attn.in_proj_bias", "model.encoder.layers.3.self_attn.out_proj.weight", "model.encoder.layers.3.self_attn.out_proj.bias", "model.encoder.layers.3.linear1.weight", "model.encoder.layers.3.linear1.bias", "model.encoder.layers.3.linear2.weight", "model.encoder.layers.3.linear2.bias", "model.encoder.layers.3.norm1.weight", "model.encoder.layers.3.norm1.bias", "model.encoder.layers.3.norm2.weight", "model.encoder.layers.3.norm2.bias", "model.encoder.layers.4.self_attn.in_proj_weight", "model.encoder.layers.4.self_attn.in_proj_bias", "model.encoder.layers.4.self_attn.out_proj.weight", "model.encoder.layers.4.self_attn.out_proj.bias", "model.encoder.layers.4.linear1.weight", "model.encoder.layers.4.linear1.bias", "model.encoder.layers.4.linear2.weight", "model.encoder.layers.4.linear2.bias", "model.encoder.layers.4.norm1.weight", "model.encoder.layers.4.norm1.bias", "model.encoder.layers.4.norm2.weight", "model.encoder.layers.4.norm2.bias", "model.encoder.layers.5.self_attn.in_proj_weight", "model.encoder.layers.5.self_attn.in_proj_bias", "model.encoder.layers.5.self_attn.out_proj.weight", "model.encoder.layers.5.self_attn.out_proj.bias", "model.encoder.layers.5.linear1.weight", "model.encoder.layers.5.linear1.bias", "model.encoder.layers.5.linear2.weight", "model.encoder.layers.5.linear2.bias", "model.encoder.layers.5.norm1.weight", "model.encoder.layers.5.norm1.bias", "model.encoder.layers.5.norm2.weight", "model.encoder.layers.5.norm2.bias", "model.encoder.norm.weight", "model.encoder.norm.bias", "model.decoder.layers.0.self_attn.in_proj_weight", "model.decoder.layers.0.self_attn.in_proj_bias", "model.decoder.layers.0.self_attn.out_proj.weight", "model.decoder.layers.0.self_attn.out_proj.bias", "model.decoder.layers.0.multihead_attn.in_proj_weight", "model.decoder.layers.0.multihead_attn.in_proj_bias", "model.decoder.layers.0.multihead_attn.out_proj.weight", "model.decoder.layers.0.multihead_attn.out_proj.bias", "model.decoder.layers.0.linear1.weight", "model.decoder.layers.0.linear1.bias", "model.decoder.layers.0.linear2.weight", "model.decoder.layers.0.linear2.bias", "model.decoder.layers.0.norm1.weight", "model.decoder.layers.0.norm1.bias", "model.decoder.layers.0.norm2.weight", "model.decoder.layers.0.norm2.bias", "model.decoder.layers.0.norm3.weight", "model.decoder.layers.0.norm3.bias", "model.decoder.layers.1.self_attn.in_proj_weight", "model.decoder.layers.1.self_attn.in_proj_bias", "model.decoder.layers.1.self_attn.out_proj.weight", "model.decoder.layers.1.self_attn.out_proj.bias", "model.decoder.layers.1.multihead_attn.in_proj_weight", "model.decoder.layers.1.multihead_attn.in_proj_bias", "model.decoder.layers.1.multihead_attn.out_proj.weight", "model.decoder.layers.1.multihead_attn.out_proj.bias", "model.decoder.layers.1.linear1.weight", "model.decoder.layers.1.linear1.bias", "model.decoder.layers.1.linear2.weight", "model.decoder.layers.1.linear2.bias", "model.decoder.layers.1.norm1.weight", "model.decoder.layers.1.norm1.bias", "model.decoder.layers.1.norm2.weight", "model.decoder.layers.1.norm2.bias", "model.decoder.layers.1.norm3.weight", "model.decoder.layers.1.norm3.bias", "model.decoder.layers.2.self_attn.in_proj_weight", "model.decoder.layers.2.self_attn.in_proj_bias", "model.decoder.layers.2.self_attn.out_proj.weight", "model.decoder.layers.2.self_attn.out_proj.bias", "model.decoder.layers.2.multihead_attn.in_proj_weight", "model.decoder.layers.2.multihead_attn.in_proj_bias", "model.decoder.layers.2.multihead_attn.out_proj.weight", "model.decoder.layers.2.multihead_attn.out_proj.bias", "model.decoder.layers.2.linear1.weight", "model.decoder.layers.2.linear1.bias", "model.decoder.layers.2.linear2.weight", "model.decoder.layers.2.linear2.bias", "model.decoder.layers.2.norm1.weight", "model.decoder.layers.2.norm1.bias", "model.decoder.layers.2.norm2.weight", "model.decoder.layers.2.norm2.bias", "model.decoder.layers.2.norm3.weight", "model.decoder.layers.2.norm3.bias", "model.decoder.layers.3.self_attn.in_proj_weight", "model.decoder.layers.3.self_attn.in_proj_bias", "model.decoder.layers.3.self_attn.out_proj.weight", "model.decoder.layers.3.self_attn.out_proj.bias", "model.decoder.layers.3.multihead_attn.in_proj_weight", "model.decoder.layers.3.multihead_attn.in_proj_bias", "model.decoder.layers.3.multihead_attn.out_proj.weight", "model.decoder.layers.3.multihead_attn.out_proj.bias", "model.decoder.layers.3.linear1.weight", "model.decoder.layers.3.linear1.bias", "model.decoder.layers.3.linear2.weight", "model.decoder.layers.3.linear2.bias", "model.decoder.layers.3.norm1.weight", "model.decoder.layers.3.norm1.bias", "model.decoder.layers.3.norm2.weight", "model.decoder.layers.3.norm2.bias", "model.decoder.layers.3.norm3.weight", "model.decoder.layers.3.norm3.bias", "model.decoder.layers.4.self_attn.in_proj_weight", "model.decoder.layers.4.self_attn.in_proj_bias", "model.decoder.layers.4.self_attn.out_proj.weight", "model.decoder.layers.4.self_attn.out_proj.bias", "model.decoder.layers.4.multihead_attn.in_proj_weight", "model.decoder.layers.4.multihead_attn.in_proj_bias", "model.decoder.layers.4.multihead_attn.out_proj.weight", "model.decoder.layers.4.multihead_attn.out_proj.bias", "model.decoder.layers.4.linear1.weight", "model.decoder.layers.4.linear1.bias", "model.decoder.layers.4.linear2.weight", "model.decoder.layers.4.linear2.bias", "model.decoder.layers.4.norm1.weight", "model.decoder.layers.4.norm1.bias", "model.decoder.layers.4.norm2.weight", "model.decoder.layers.4.norm2.bias", "model.decoder.layers.4.norm3.weight", "model.decoder.layers.4.norm3.bias", "model.decoder.layers.5.self_attn.in_proj_weight", "model.decoder.layers.5.self_attn.in_proj_bias", "model.decoder.layers.5.self_attn.out_proj.weight", "model.decoder.layers.5.self_attn.out_proj.bias", "model.decoder.layers.5.multihead_attn.in_proj_weight", "model.decoder.layers.5.multihead_attn.in_proj_bias", "model.decoder.layers.5.multihead_attn.out_proj.weight", "model.decoder.layers.5.multihead_attn.out_proj.bias", "model.decoder.layers.5.linear1.weight", "model.decoder.layers.5.linear1.bias", "model.decoder.layers.5.linear2.weight", "model.decoder.layers.5.linear2.bias", "model.decoder.layers.5.norm1.weight", "model.decoder.layers.5.norm1.bias", "model.decoder.layers.5.norm2.weight", "model.decoder.layers.5.norm2.bias", "model.decoder.layers.5.norm3.weight", "model.decoder.layers.5.norm3.bias", "model.decoder.norm.weight", "model.decoder.norm.bias", "model.vocab_head.weight", "model.vocab_head.bias", "model.pointer_head.weight", "model.pointer_head.bias", "model.switch_head.weight", "model.switch_head.bias".

Input images

Hello,

I could run all steps with your data from the preparation until the visualization, and it looks very nice.

However, I still don't understand where to start with my own images which are raster images in jpg format. In your branch dedicated to raster images, I only see svg or json files but not the original images.

Could you please provide me a few precisions on this topic?

Thanks!

Best,
Guillaume

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