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Pix2Vox-ResNet

This project is part of the course "Machine Learning for 3D Geometry" from Technical University of Munich, offered in Summer Semester 2022.

Overview (source: https://github.com/hzxie/Pix2Vox)

This project is largely based on previous work from Pix2Vox (Xie et al. 2019). Pix2Vox is an encoder-decoder-based framework for single-view or multi-view 3D shape reconstruction. It utilizes a pre-trained VGG-16 as part of its encoder to extract visual representations from the input images. In our project, we conduct an ablation study on Pix2Vox’s encoder by replacing the pre-trained VGG-16 with a pre-trained ResNet-152, which has been shown to gain higher accuracy on the ImageNet benchmark. Our aim is to verify whether learned 2D features from pre-trained models with better accuracy on ImageNet can be used to generate 3D shape reconstructions of better quality. Experimental results on the ShapeNet benchmark indicate that the model using ResNet-152 gains an absolute IoU increase of 0.3% when using more than 12 views, while underperforming the model using VGG-16 when a small amount of views is used.

Teams

  • Cuong Nguyen
  • Michelle Espranita Liman
  • Thang Tran
  • Viet Nguyen

Test Results

Single View Reconstruction

Category 3D-R2N2 Pix2Vox-F Pix2Vox-A Ours
airplane 0.513 0.600 0.684 0.624
bench 0.421 0.538 0.616 0.541
cabinet 0.716 0.765 0.792 0.762
car 0.798 0.837 0.854 0.833
chair 0.466 0.535 0.567 0.532
display 0.468 0.511 0.537 0.502
lamp 0.381 0.435 0.443 0.432
speaker 0.662 0.707 0.714 0.691
rifle 0.544 0.598 0.615 0.571
sofa 0.628 0.687 0.709 0.684
table 0.513 0.587 0.601 0.575
telephone 0.661 0.770 0.776 0.740
watercraft 0.513 0.582 0.594 0.563
Overall 0.560 0.634 0.661 0.628
  • Single-view reconstruction on ShapeNet compared using Intersection-over-Union (IoU). The best number for each category is highlighted in bold. Ours is Pix2Vox-A with pre-trained ResNet152 encoders.

Multiview Reconstruction

Method 1 view 2 views 3 views 4 views 5 views 8 views 12 views 16 views 20 views
3D-R2N2 0.560 0.603 0.617 0.625 0.634 0.635 0.636 0.636 0.636
Pix2Vox-F 0.634 0.660 0.668 0.673 0.676 0.680 0.682 0.684 0.684
Pix2Vox-A 0.661 0.686 0.693 0.697 0.699 0.702 0.704 0.705 0.706
Ours 0.628 0.669 0.683 0.690 0.695 0.702 0.706 0.708 0.709
  • Multi-view reconstruction on ShapeNet compared using Intersection-over-Union (IoU). The best results for different numbers of views are highlighted in bold. Ours is Pix2Vox-A with pre-trained ResNet-152 encoders.

Rendered Reconstruction

Overview

  • Reconstruction results of Pix2Vox-A and our method on the ShapeNet test set using 1 view, 12 views, and 20 views. GT represents the ground truth of the 3D object. We observe that our method produces a better reconstruction using 20 views, but performs worse using 1 view.