Neural Continuous Spatiotemporal Fields Made Easy with ResFields
Neural continuous spatiotemporal fields, also known as neural fields, are best represented by the multi-layer perceptron (MLP) neural network architecture. MLPs have become incredibly popular due to their ability to encode continuous signals over any dimension, their implicit regularization, and their effective interpolation. These features have led to their success in various applications like image synthesis, animation, texture creation, and innovative view synthesis. However, there is a problem when it comes to collecting fine-grained features and replicating complex real-world signals. MLPs have a spectral bias, which means they tend to learn functions with low frequencies.
Previous attempts to overcome this bias involved positional encoding and unique activation functions, but even with these techniques, capturing fine-grained features is challenging, especially with large spatiotemporal data like lengthy films or dynamic 3D scenes. Increasing the complexity of the network by adding more neurons may boost the capacity of MLPs, but it also slows down inference and optimization and requires more GPU RAM.
The researchers from ETH Zurich, Microsoft, and the University of Zurich wanted to tackle this problem of increasing model capacity without compromising the architecture, input encoding, or activation functions of MLP neural fields. They also wanted to maintain the implicit regularization property of neural networks and build on existing methods for reducing spectral bias. Their solution was to replace one or more MLP layers with time-dependent layers that have trainable residual parameters. They call the neural fields created in this way ResFields.
Another option they considered was meta-learning MLP weights and maintaining separate parameters. However, this approach requires long training that doesn’t scale well to photo-realistic reconstructions. Partitioning the spatiotemporal field and fitting different neural areas is a common technique for boosting modeling capability, but it hinders global reasoning and generalization. These techniques are crucial for radiance field reconstruction from sparse views. The main benefits of using ResFields to increase model capacity are that inference and training speed are maintained, the implicit regularization and generalization capabilities of MLPs are preserved, and ResFields are adaptable and work with most MLP-based algorithms for spatiotemporal data. However, the simple implementation of ResFields may lead to diminished interpolation qualities.
The researchers suggest implementing the residual parameters as a global low-rank spanning set and a set of time-dependent coefficients, inspired by low-rank factorized layers. This modeling improves generalization qualities and significantly reduces the memory required to store extra network parameters.
In summary, the main contributions of this research are: the introduction of ResFields as an architecture-independent building component for modeling spatiotemporal fields, a systematic demonstration of how their approach enhances existing methods, and the achievement of cutting-edge results for tasks like neural-radiance field reconstruction, temporal 3D form modeling, and 2D video approximation.
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About the Author:
Aneesh Tickoo is a consulting intern at MarktechPost. He is studying Data Science and Artificial Intelligence at the Indian Institute of Technology(IIT), Bhilai. Aneesh is passionate about machine learning and spends his time working on projects in image processing. He loves collaborating with others on interesting projects.