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Haipeng Guo

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3 papers

1 author row

AAAI Conference 2025 Conference Paper

Cross-Spectral Gaussian Splatting with Spatial Occupancy Consistency

Haipeng Guo
Huanyu Liu
Jiazheng Wen
Junbao Li

Using images captured by cameras with different light spectrum sensitivities, training a unified model for cross-spectral scene representation is challenging. Recent advances have shown the possibility of jointly optimizing cross-spectral relative poses and neural radiance fields using normalized cross-device coordinates. However, such method suffers from cross-spectral misalignment when collecting data asynchronously from devices and lacks the capability to render in real-time or handle large scenes. We address these issues by proposing cross-spectral Gaussian Splatting with spatial occupancy consistency, strictly aligns cross-spectral scene representation by sharing explicit Gaussian surfaces across spectra and separately optimizing each view's extrinsic using a matching-optimizing pose estimation method. Additionally, to address field-of-view differences in cross-spectral cameras, we improve the adaptive densify controller to fill non-overlapping areas. Comprehensive experiments demonstrate that SOC-GS achieves superior performance in novel view synthesis and real-time cross-spectral rendering.

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AAAI Conference 2002 Short Paper

A Bayesian Metareasoner for Algorithm Selection for Real-Time Bayesian Network Inference Problems

Haipeng Guo

The aim of this research is to integrate various Bayesian network (BN) inference algorithms into a framework based on Bayesian methods to solving the "algorithm selection problem" of real-time BN inference. The metareasoner is a Bayesian network that encodes the uncertain knowledge of dependencies among the characteristics of BN inference problem instances and the performance of the inference algorithms. It is automatically learned from some representative synthetic training data with the guidance of some domain nowledge. Once having this metareasoner network, we can then use it to select the right algorithm for a given Bayesian network inference problem instance and predict the run time performance of the algorithm on this problem instance. Such methods will also be useful in solving other hard problems.

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AAAI Conference 2002 Short Paper

A Genetic Algorithm for Tuning Variable Orderings in Bayesian Network Structure Learning

Haipeng Guo
Julie A. Stilson

Learning a Bayesian network from data is NP-hard even without considering unobserved or irrelevant variables. Many previous Bayesian network learning algorithms require that a node ordering is available before learning. Unfortunately, this is usually not the case in many real-world applications. To make greedy search usable when node orderings are unknown, we have developed a permutation genetic algorithm (GA) wrapper to tune the variable ordering given as input to K2, a score-based BN learning algorithm. We have used a probabilistic inference criterion as the GA’s fitness function and we are also trying some other criterion to evaluate the learning result such as the learning fixed-point property.

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