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1,448 results on '"Liu, Shilin"'

8. Binned Likelihood including Monte Carlo Statistical Uncertainty in Bayesian Inference

Author

Liu, Shilin and McGrew, Clark

Subjects
High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract

Data analysis in HEP experiments often uses binned likelihood from data and finite Monte Carlo sample. Statistical uncertainty of Monte Carlo sample has been introduced in Frequentist Inference in some literatures, but they are not suitable for Bayesian Inference. This technical note introduces the binned likelihood with Monte Carlo statistical uncertainty in Bayesian Inference and includes the derivation of it. It turns out that the results are similar to the results in [1]. But this tech-note gives an alternate and more intuitive derivation of the content

Published
2023

12. ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation

Author

Guo, Haoxiang, Liu, Shilin, Pan, Hao, Liu, Yang, Tong, Xin, and Guo, Baining

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics, I.3.5, I.2.10
Abstract

We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders, i.e. vertices, edges and surface patches, and the correspondence of primitives, which are holistically modeled as a chain complex, and show that by modeling such comprehensive structures more complete and regularized reconstructions can be achieved. We solve the complex generation problem in two steps. First, we propose a novel neural framework that consists of a sparse CNN encoder for input point cloud processing and a tri-path transformer decoder for generating geometric primitives and their mutual relationships with estimated probabilities. Second, given the probabilistic structure predicted by the neural network, we recover a definite B-Rep chain complex by solving a global optimization maximizing the likelihood under structural validness constraints and applying geometric refinements. Extensive tests on large scale CAD datasets demonstrate that the modeling of B-Rep chain complex structure enables more accurate detection for learning and more constrained reconstruction for optimization, leading to structurally more faithful and complete CAD B-Rep models than previous results., Comment: This article is published by ACM Trans. Graph. (SIGGRAPH 2022). This is the author's preprint version

Published
2022

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