Learning to Generate 3D Training Data through Hybrid Gradient
Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform well on real images, because a graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and the placement of the camera. In this work, we propose a new method that optimizes the generation of 3D training data based on what we call *hybrid gradient*. We parametrize the design decisions as a real vector, and combine the approximate gradient and the analytical gradient to obtain the hybrid gradient of the network performance with respect to this vector. We evaluate our approach on the task of estimating surface normal and depth from a single image. Experiments on standard benchmarks show that our approach can outperform the prior state of the art on optimizing the generation of 3D training data, particularly in terms of computational efficiency.
The paper presents a method for optimizing synthetically-generated scenes for transferability to real scenes. The synthetic scene is parameterized by a vector of parameters (beta), and the Authors propose a method to optimize the evaluation loss with respect to beta by gradient descent. The key contribution is a method for calculating the gradient of the evaluation loss with respect to beta, which is done half-analytically (by differentiating the evaluation loss with respect to the training images using unrolled SGD) and half-numerically (differentiating the training images with respect to beta by sampling in the beta space). The idea is conceptually simple and neat, but practically it seems fairly complicated to chain all these derivatives together to obtain an accurate-enough estimate of the gradient for gradient-descent. It is very impressive IMO that in the end of the day this loop can indeed be closed, and synthetic scenes can be automatically optimized for transferability to real data, as the results prove. Given the great importance of sim-to-real in computer vision and robotics, the neatness of the method, the strong results, and the clear presentation, I am rating this paper *accept*. At the same time, I admit that I have little experience myself with sim-to-real problems and generating simulated environments, and it is thus harder for me to assess the novelty and the comparison of this approach to others.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning to Generate 3D Training Data through Hybrid Gradient
Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform well on real images, because a graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and the placement of the camera. In this work, we propose a new method that optimizes the generation of 3D training data based on what we call *hybrid gradient*. We parametrize the design decisions as a real vector, and combine the approximate gradient and the analytical gradient to obtain the hybrid gradient of the network performance with respect to this vector. We evaluate our approach on the task of estimating surface normal and depth from a single image. Experiments on standard benchmarks show that our approach can outperform the prior state of the art on optimizing the generation of 3D training data, particularly in terms of computational efficiency.
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 presents a method for optimizing synthetically-generated scenes for transferability to real scenes. The synthetic scene is parameterized by a vector of parameters (beta), and the Authors propose a method to optimize the evaluation loss with respect to beta by gradient descent. The key contribution is a method for calculating the gradient of the evaluation loss with respect to beta, which is done half-analytically (by differentiating the evaluation loss with respect to the training images using unrolled SGD) and half-numerically (differentiating the training images with respect to beta by sampling in the beta space). The idea is conceptually simple and neat, but practically it seems fairly complicated to chain all these derivatives together to obtain an accurate-enough estimate of the gradient for gradient-descent. It is very impressive IMO that in the end of the day this loop can indeed be closed, and synthetic scenes can be automatically optimized for transferability to real data, as the results prove. Given the great importance of sim-to-real in computer vision and robotics, the neatness of the method, the strong results, and the clear presentation, I am rating this paper *accept*. At the same time, I admit that I have little experience myself with sim-to-real problems and generating simulated environments, and it is thus harder for me to assess the novelty and the comparison of this approach to others.
assistant
The paper presents a method for optimizing synthetically-generated scenes for transferability to real scenes. The synthetic scene is parameterized by a vector of parameters (beta), and the Authors propose a method to optimize the evaluation loss with respect to beta by gradient descent. The key contribution is a method for calculating the gradient of the evaluation loss with respect to beta, which is done half-analytically (by differentiating the evaluation loss with respect to the training images using unrolled SGD) and half-numerically (differentiating the training images with respect to beta by sampling in the beta space). The idea is conceptually simple and neat, but practically it seems fairly complicated to chain all these derivatives together to obtain an accurate-enough estimate of the gradient for gradient-descent. It is very impressive IMO that in the end of the day this loop can indeed be closed, and synthetic scenes can be automatically optimized for transferability to real data, as the results prove. Given the great importance of sim-to-real in computer vision and robotics, the neatness of the method, the strong results, and the clear presentation, I am rating this paper *accept*. At the same time, I admit that I have little experience myself with sim-to-real problems and generating simulated environments, and it is thus harder for me to assess the novelty and the comparison of this approach to others.