arXiv subjects

Jiale Wu

Publications and source records attributed to Jiale Wu.

3 recordsLinked to original sources

arXiv2026

PhyGaP: Physically-Grounded Gaussians with Polarization Cues

Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated great success in modeling reflective 3D objects and their interaction with the environment via deferred rendering (DR). However, existing methods often struggle with correctly reconstructing physical attributes such as albedo and reflectance, and therefore they do not support high-fidelity relighting. Observing that this limitation stems from the lack of shape and material information in RGB images, we present PhyGaP, a physically-grounded 3DGS method that leverages polarization cues to facilitate precise reflection decomposition and visually consistent relighting of reconstructed objects. Specifically, we design a polarimetric deferred rendering (PolarDR) process to model polarization by reflection, and a self-occlusion-aware environment map building technique (GridMap) to resolve indirect lighting of non-convex objects. We validate on multiple synthetic and real-world scenes, including those featuring only partial polarization cues, that PhyGaP not only excels in reconstructing the appearance and surface normal of reflective 3D objects (~2 dB in PSNR and 45.7% in Cosine Distance better than existing RGB-based methods on average), but also achieves state-of-the-art inverse rendering and relighting capability. Our code will be released soon.

cs.CV↗

arXiv2025

Diffusion Distillation With Direct Preference Optimization For Efficient 3D LiDAR Scene Completion

The application of diffusion models in 3D LiDAR scene completion is limited due to diffusion's slow sampling speed. Score distillation accelerates diffusion sampling but with performance degradation, while post-training with direct policy optimization (DPO) boosts performance using preference data. This paper proposes Distillation-DPO, a novel diffusion distillation framework for LiDAR scene completion with preference aligment. First, the student model generates paired completion scenes with different initial noises. Second, using LiDAR scene evaluation metrics as preference, we construct winning and losing sample pairs. Such construction is reasonable, since most LiDAR scene metrics are informative but non-differentiable to be optimized directly. Third, Distillation-DPO optimizes the student model by exploiting the difference in score functions between the teacher and student models on the paired completion scenes. Such procedure is repeated until convergence. Extensive experiments demonstrate that, compared to state-of-the-art LiDAR scene completion diffusion models, Distillation-DPO achieves higher-quality scene completion while accelerating the completion speed by more than 5-fold. Our method is the first to explore adopting preference learning in distillation to the best of our knowledge and provide insights into preference-aligned distillation. Our code is public available on https://github.com/happyw1nd/DistillationDPO.

cs.CV↗

arXiv2024

Bounded-Time Nonblocking Supervisory Control of Timed Discrete-Event Systems

Recently an automaton property of quantitative nonblockingness was proposed in supervisory control of untimed discrete-event systems (DES), which quantifies the standard nonblocking property by capturing the practical requirement that all tasks be completed within a bounded number of steps. However, in practice tasks may be further required to be completed in specific time. To meet this new requirement, in this paper we introduce the concept of bounded-time nonblockingness, which extends the concept of quantitative nonblockingness from untimed DES to timed DES. This property requires that each task must be completed within a bounded time counted by the number of ticks, rather than bounded number of transition steps in quantitative nonblockingness. Accordingly, we formulate a new bounded-time nonblocking supervisory control problem (BTNSCP) of timed DES, and characterize its solvability in terms of a new concept of bounded-time language completability. Then we present an approach to compute the maximally permissive solution to the new BTNSCP.

eess.SY↗