A Simple and Scalable Shape Representation for 3D Reconstruction
Deep learning applied to the reconstruction of 3D shapes has seen growing interest. A popular approach to 3D reconstruction and generation in recent years has been the CNN decoder-encoder model often applied in voxel space. However this often scales very poorly with the resolution limiting the effectiveness of these models. Several sophisticated alternatives for decoding to 3D shapes have been proposed typically relying on alternative deep learning architectures. We show however in this work that standard benchmarks in 3D reconstruction can be tackled with a surprisingly simple approach: a linear decoder obtained by principal component analysis on the signed distance transform of the surface. This approach allows easily scaling to larger resolutions. We show in multiple experiments it is competitive with state of the art methods and also allows the decoder to be fine-tuned on the target task using a loss designed for SDF transforms, obtaining further gains.
The paper introduces a 3D object reconstruction/completion algorithm that utilizes a simple decoder from features generated using PCA of SDF. The approach was tested in a few experiments on public benchmarks and achieves competitive results. The overall presentation of the paper is decent, and the network structure of the proposed approach is reasonable. It*s interesting to see that using a simple PCA can help improve performance using a simple network structure. The experimental results make sense, and it*s nice to see the performance is reasonable as well. I have a few questions regarding the paper: - Without looking at the code, I don*t think I fully understand the formulation of the network structure just by reading the text. For example, what is the dashed line mean in Fig. 1? What*s the meaning of the dot over *E* in Sec 3.2, is it *derivatives*? If so, why not use this symbol in Eq. (2) as well? In general, I find it*s a bit hard to follow when only 2-3 paragraphs are used for describing the proposed approach. It*ll be good if the authors can elaborate on the approach in a more thorough manner. - Regarding experimental results, it*s nice to see that eigenSDF is better than linearSDF, demonstrating that the approach is quite effective. However it is not always the best in several metrics (as indicated in experimental result tables). I wonder if authors can provide more analysis or discussions on why this could happen, either the metric may not make too much sense in their setting, or if there is potential room for improvement. A few failure case visualizations could also be helpful in understanding the issues of the proposed approach. - Moreover, do authors have thoughts on eigenSDF vs deepSDF (cited in the paper, published in CVPR 2019)? It*ll be interesting to compare those as well, as deepSDF has proven useful in a few papers already.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A Simple and Scalable Shape Representation for 3D Reconstruction
Deep learning applied to the reconstruction of 3D shapes has seen growing interest. A popular approach to 3D reconstruction and generation in recent years has been the CNN decoder-encoder model often applied in voxel space. However this often scales very poorly with the resolution limiting the effectiveness of these models. Several sophisticated alternatives for decoding to 3D shapes have been proposed typically relying on alternative deep learning architectures. We show however in this work that standard benchmarks in 3D reconstruction can be tackled with a surprisingly simple approach: a linear decoder obtained by principal component analysis on the signed distance transform of the surface. This approach allows easily scaling to larger resolutions. We show in multiple experiments it is competitive with state of the art methods and also allows the decoder to be fine-tuned on the target task using a loss designed for SDF transforms, obtaining further gains.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper introduces a 3D object reconstruction/completion algorithm that utilizes a simple decoder from features generated using PCA of SDF. The approach was tested in a few experiments on public benchmarks and achieves competitive results. The overall presentation of the paper is decent, and the network structure of the proposed approach is reasonable. It*s interesting to see that using a simple PCA can help improve performance using a simple network structure. The experimental results make sense, and it*s nice to see the performance is reasonable as well. I have a few questions regarding the paper: - Without looking at the code, I don*t think I fully understand the formulation of the network structure just by reading the text. For example, what is the dashed line mean in Fig. 1? What*s the meaning of the dot over *E* in Sec 3.2, is it *derivatives*? If so, why not use this symbol in Eq. (2) as well? In general, I find it*s a bit hard to follow when only 2-3 paragraphs are used for describing the proposed approach. It*ll be good if the authors can elaborate on the approach in a more thorough manner. - Regarding experimental results, it*s nice to see that eigenSDF is better than linearSDF, demonstrating that the approach is quite effective. However it is not always the best in several metrics (as indicated in experimental result tables). I wonder if authors can provide more analysis or discussions on why this could happen, either the metric may not make too much sense in their setting, or if there is potential room for improvement. A few failure case visualizations could also be helpful in understanding the issues of the proposed approach. - Moreover, do authors have thoughts on eigenSDF vs deepSDF (cited in the paper, published in CVPR 2019)? It*ll be interesting to compare those as well, as deepSDF has proven useful in a few papers already.
assistant
The paper introduces a 3D object reconstruction/completion algorithm that utilizes a simple decoder from features generated using PCA of SDF. The approach was tested in a few experiments on public benchmarks and achieves competitive results. The overall presentation of the paper is decent, and the network structure of the proposed approach is reasonable. It*s interesting to see that using a simple PCA can help improve performance using a simple network structure. The experimental results make sense, and it*s nice to see the performance is reasonable as well. I have a few questions regarding the paper: - Without looking at the code, I don*t think I fully understand the formulation of the network structure just by reading the text. For example, what is the dashed line mean in Fig. 1? What*s the meaning of the dot over *E* in Sec 3.2, is it *derivatives*? If so, why not use this symbol in Eq. (2) as well? In general, I find it*s a bit hard to follow when only 2-3 paragraphs are used for describing the proposed approach. It*ll be good if the authors can elaborate on the approach in a more thorough manner. - Regarding experimental results, it*s nice to see that eigenSDF is better than linearSDF, demonstrating that the approach is quite effective. However it is not always the best in several metrics (as indicated in experimental result tables). I wonder if authors can provide more analysis or discussions on why this could happen, either the metric may not make too much sense in their setting, or if there is potential room for improvement. A few failure case visualizations could also be helpful in understanding the issues of the proposed approach. - Moreover, do authors have thoughts on eigenSDF vs deepSDF (cited in the paper, published in CVPR 2019)? It*ll be interesting to compare those as well, as deepSDF has proven useful in a few papers already.