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Wayne State University

Academic institutionnorthamerica · us
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Research library113linked papers
Opportunities0open roles
Selected work

Representative Papers

Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation

Mar 31, 2025

To address the challenge of learning signed distance functions (SDFs) from sparse point clouds—where insufficient geometric detail impairs surface reconstruction—this paper proposes an end-to-end dynamic deformation framework. The method jointly optimizes an explicit parametric surface and an implicit SDF field through three core components: (1) a bijective surface parameterization (BSP) that establishes invertible mappings between local surface patches and the global shape; (2) a grid-based deformation optimization (GDO) strategy that co-refines both the parameterized surface and the implicit field; and (3) a synergistic learning mechanism integrating bijective neural mappings, local patch embeddings, and differentiable rendering. Evaluated on both synthetic and real-world scanned datasets, the approach achieves significant improvements in SDF reconstruction accuracy and topological consistency over state-of-the-art methods.

3 citationsRead paper

ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms

Jan 13, 2026

This work addresses the limitations of traditional dynamic voltage and frequency scaling (DVFS) and task-core allocation methods, which rely on heuristics or offline profiling, struggle to generalize to unseen workloads, and neglect stall time—leading to suboptimal energy efficiency and thermal management. To overcome these challenges, the paper proposes the first large language model (LLM)-guided, zero-shot multi-agent reinforcement learning framework for runtime scheduling. The approach leverages an LLM to extract 13-dimensional code-level semantic features from OpenMP programs and integrates hierarchical multi-agent action decomposition, regression-based environment modeling, and a Dyna-Q architecture to enable workload-agnostic scheduling without prior profiling. Experiments on Jetson TX2/Orin NX, RubikPi, and Intel Core i7 platforms demonstrate a 7.09× improvement in energy efficiency and a 4× reduction in task completion time compared to the Linux ondemand scheduler, with the first scheduling decision made 8,300× faster than conventional tabular methods.

2 citationsRead paper

GaussianUDF: Inferring Unsigned Distance Functions through 3D Gaussian Splatting

Mar 25, 2025

Existing 3D Gaussian Splatting (3DGS) struggles to reconstruct open surfaces from multi-view images due to incompatibility between its discrete, explicit representation and the continuous, implicit modeling of Unsigned Distance Functions (UDFs). Method: This paper proposes the first joint 3DGS-UDF learning framework. It introduces a differentiable 2D Gaussian plane parameterization to represent local surface geometry, enables UDF gradient estimation via self-supervised gradient inference, and imposes a zero-level-set neighborhood constraint to stabilize optimization. The framework is fully end-to-end differentiable and requires no depth maps, masks, or voxel supervision. Results: Evaluated on standard benchmarks and real-world scenes, the method significantly improves reconstruction completeness, boundary sharpness, and geometric accuracy, while maintaining efficient training and real-time rendering. It achieves state-of-the-art performance across all major metrics, outperforming existing approaches.

2 citationsRead paper
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