Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: In this paper, the authors provide a novel idea for shape reconstruction from a single image. They  enable the idea and techniques developed in the few-shot learning literature to the problem of shape reconstruction. They construct the meta-train data by a point generation network, and utilize a differential learner to learn the decision boundary.  They model the reconstruct problem by bi-level optimization.  It is an interesting work. The results are also good. 
main_review: This is a good work. There are several strengths. 
1) The idea that converting shape reconstruction from image into few-shot learning problem is novel.
2) The authors generalize the differential learner with kernelized algorithms.
3) The results are better then previous works.

There are also some problems needed feedback from the authors.
a) The author use EfficientNetB1 as the Feature Network, and how about utilizing other architecture for the Feature Network? Such as ResNet? 
b) The author  generalize the differential learner with kernelized algorithms. There should be experimental result that without kernel skills.
c) More (visual) results on other dataset are needed, such as KITTI and Online Products datasets. 
summary_of_the_review: This is a good paper with novel idea. More results are needed to make it better.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes a method for 3D shape reconstruction from image using a bi-level optimization technique. Specifically, the first part of the proposed network extract features from the image and creates a set of 3D points sampled near the surface of the shape to be reconstructed with labels corresponding to the points being inside or outside the 3D shape boundary. Then, the produced sets of points are fed to the second part of the network which is trained to approximate an implicit representation of the shape. At the inference time, a 2D surface of the shape can be extracted from the predicted implicit representation.

main_review: Strengths of the paper:
- The paper is clearly written, and one can implement the proposed approach using the algorithm description in the paper.
- It proposes a new technique to reconstruct 3D shapes from 2D images, which obtains results comparable or better (in terms of standard metrics) than state of the art on a subset of the ShapeNet dataset.
- The proposed reconstruction problem decomposition (described in the previous section) is interesting and novel, albeit not clearly motivated (See paper weaknesses description).

Weaknesses of the paper:
- The paper does not clearly articulate the motivation for the proposed approach, i.e. why it is beneficial to formulate the reconstruction problem as a meta-learning problem. It should also provide a more in-depth comparison of the proposed method with the existing methods which directly predict the implicit shape representation, in order to explain why the proposed formulation outperforms these methods.
- I may be missing it - but nowhere in the paper there is a clear description of how the inference is performed, after the network has been trained. Please add it, e.g., at the end of Section 3.2.
- In the proposed method description at the beginning of Section 3, it is worth clarifying that the output of the Point Generation Network is in fact the training set for the second part of the network, as mentioned in the introduction.
- It would be interesting to perform an ablation study on dimension of the embedding space.
- Figure 10 present an example of shape interpolation. Have the authors tried to interpolate shapes with different topologies and were the results plausible?

Small comments and typos:
- There is a wrong line break at the beginning of Section 5.
- The caption for Figure 11 is too long - it should be shortened and the text should be moved to the corresponding appendix.
summary_of_the_review: The paper proposes a novel method, inspired by meta-learning algorithms, for 3D shape reconstruction from images. The proposed method produces results which are comparable or better than the current state of the art approaches. On the other hand, the paper does not provide  a clear explanation why the proposed approach is suitable for the problem of shape reconstruction and why it is expected to perform better than approaches employing a single network. Adding such an explanation, and a more in-depth analysis of the improvements brought by the bi-level optimization will significantly improve the paper. Currently I recommend "weak accept".
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper introduce a few shot learning method for single view reconstruction task. Different from traditional few shot learning methods, data used for training are generated by a network which takes an image as input. Both qualitative and quantitative reconstruction results show that this method gives a better performance than current SOTA methods at less training data(occupancy or distance field ground truth). 

main_review: Please Bist both the strengths and weaknesses of the paper. When discussing weakness, please provide concrete. actionable feedback on the paper.

To avoid fully sample points and its distance value to the surface over the 3D space in training, the paper proposes a meta learning approach. It is a novel idea in computer graphics because different from datasets in computer vision or natural language processing, 3D shape dataset is far smaller. However, I still have several concerns. 

- The proposed architecture uses a point generation network to generate training data for few shot learning. I am wondering why not directly sample a few points on and near the shape surface to train the few shot network?

- Predicted points are not guaranteed to be exactly those points you have sampled in the offline setting. So how do you get the occupancy value at these points in the training process?

- Even though the author gives the reason for R^3 embedding space, I am wondering if how the embedding space dimension will influence the results.
summary_of_the_review: I am generally positive about this paper since it proposes a novel method to single view reconstruction using few shot learning. But it needs a more detailed illustration about the training process and more evaluations on the design choice. If the authors can resolve those issues I mentioned above, I am willing to give a higher score.
review_title: null

Meta-review: All reviewers recommended accept after author responses. AC doesn't find any reason to overturn this consensus.
Decision: Accept (Poster)