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ABSTRACT We introduce MF-Box, an extended version of MFEmulator, designed as a fast surrogate for power spectra, trained using N-body simulation suites from various box sizes and particle loads. To demonstrate MF-Box’s effectiveness, we design simulation suites that include low-fidelity (LF) suites (L1 and L2) at 256 and $$100 \, \rm {Mpc\, ~}h^{-1}$$, each with 1283 particles, and a high-fidelity (HF) suite with 5123 particles at $$256 \, \rm {Mpc\, ~}h^{-1}$$, representing a higher particle load compared to the LF suites. MF-Box acts as a probabilistic resolution correction function, learning most of the cosmological dependencies from L1 and L2 simulations and rectifying resolution differences with just three HF simulations using a Gaussian process. MF-Box successfully emulates power spectra from our HF testing set with a relative error of $$\lt 3~{{\ \rm per\ cent}}$$ up to $$k \simeq 7 \, h\rm {Mpc}{^{-1}}$$ at z ∈ [0, 3], while maintaining a cost similar to our previous multifidelity approach, which was accurate only up to z = 1. The addition of an extra LF node in a smaller box significantly improves emulation accuracy for MF-Box at $$k \gt 2 \, h\rm {Mpc}{^{-1}}$$, increasing it by a factor of 10. We conduct an error analysis of MF-Box based on computational budget, providing guidance for optimizing budget allocation per fidelity node. Our proposed MF-Box enables future surveys to efficiently combine simulation suites of varying quality, effectively expanding the range of emulation capabilities while ensuring cost efficiency.
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Shape and Material Capture at Home
In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capture technique in which the user goes around the object, illuminating it with a flashlight and capturing only a few images. Our main technical contribution is the introduction of a recursive neural architecture, which can predict geometry and reflectance at 2 k ×2 k resolution given an input image at 2 k ×2 k and estimated geometry and reflectance from the previous step at 2 k−1 ×2 k−1 . This recursive architecture, termed RecNet, is trained with 256×256 resolution but can easily operate on 1024×1024 images during inference. We show that our method produces more accurate surface normal and albedo, especially in regions of specular highlights and cast shadows, compared to previous approaches, given three or fewer input images.
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- Award ID(s):
- 1910132
- PAR ID:
- 10347184
- Date Published:
- Journal Name:
- Computer Vision and Pattern Recognition
- Page Range / eLocation ID:
- 6119 to 6129
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CVPR46437.2021.00606
