arXiv subjects

Yonghao Zhao

Publications and source records attributed to Yonghao Zhao.

4 recordsLinked to original sources

arXiv2026

SRUG: Shadow-Guided Relightable Urban Scene with Generation Model

Creating relightable urban scenes from images or videos is widely useful but highly ill-posed. Urban environments are typically unbounded and extend beyond the visible regions. As a result, many portions of the scene remain unobserved, yet these invisible regions can cast shadows onto visible areas. Reasonably modeling shadows cast by such invisible regions is challenging and poses a significant obstacle to creating relightable urban scenes. At the same time, sparse input views and complex illumination conditions further complicate relighting, as they introduce severe ambiguities in material decomposition. In this paper, we propose Shadow-guided Relightable Urban Scene with Generation model (SRUG), a novel framework designed to address relighting challenges in urban scenes. SRUG leverages shadows to guide a 3D completion model for recovering the geometry of invisible regions, promoting the synthesis of physically reasonable shadows. In addition, SRUG employs an iterative material decomposition scheme that applies the large material model (LMM) to provide material supervision and iteratively decompose the scene's material properties, enabling robust material decomposition. Building upon these components, we introduce a physically-based lighting model that captures the complex illumination of urban scenes and supports reliable relighting. Extensive quantitative evaluations and visual comparisons demonstrate that our method outperforms existing approaches in both novel view synthesis and relighting tasks.

cs.CV↗

arXiv2026

GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space

Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made it standard practice to reconstruct 3D scenes from multi-view images. Removing objects from such 3D representations is a fundamental editing task that requires complete and seamless inpainting of occluded regions, ensuring consistency in geometry and appearance. Although existing methods have made notable progress in improving inpainting consistency, they often neglect global lighting effects, leading to physically implausible results. Moreover, these methods struggle with view-dependent non-Lambertian surfaces, where appearance varies across viewpoints, leading to unreliable inpainting. In this paper, we present 3D Gaussian Object Removal in the Intrinsic Space (GOR-IS), a novel framework for physically consistent and visually coherent 3D object removal. Our approach decomposes the scene into intrinsic components and explicitly models light transport to maintain global lighting effects consistency. Furthermore, we introduce an intrinsic-space inpainting module that operates directly in the material and lighting domains, effectively addressing the challenges posed by non-Lambertian surfaces. Extensive experiments on both synthetic and real-world datasets demonstrate that our framework substantially improves the physical consistency and visual coherence of object removal, outperforming existing methods by 13% in perceptual similarity (LPIPS) and 2dB in peak signal-to-noise ratio (PSNR). Code is publicly available at https://applezyh.github.io/GOR-IS-project-page/

cs.CV↗

arXiv2025

Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis

Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce useful patterns for survival predictions. This study deals with small sample survival analysis by leveraging transfer learning, a useful machine learning technique that can enhance the target analysis with related knowledge pre-learned from other data. We propose and develop various transfer learning methods designed for common survival models. For parametric models such as DeepSurv, Cox-CC (Cox-based neural networks), and DeepHit (end-to-end deep learning model), we apply standard transfer learning techniques like pretraining and fine-tuning. For non-parametric models such as Random Survival Forest, we propose a new transfer survival forest (TSF) model that transfers tree structures from source tasks and fine-tunes them with target data. We evaluated the transfer learning methods on colorectal cancer (CRC) prognosis. The source data are 27,379 SEER CRC stage I patients, and the target data are 728 CRC stage I patients from the West China Hospital. When enhanced by transfer learning, Cox-CC's $C^{td}$ value was boosted from 0.7868 to 0.8111, DeepHit's from 0.8085 to 0.8135, DeepSurv's from 0.7722 to 0.8043, and RSF's from 0.7940 to 0.8297 (the highest performance). All models trained with data as small as 50 demonstrated even more significant improvement. Conclusions: Therefore, the current survival models used for cancer prognosis can be enhanced and improved by properly designed transfer learning techniques. The source code used in this study is available at https://github.com/YonghaoZhao722/TSF.

cs.LG↗

arXiv2017

Origins and dissociation of pyramidal dislocations in magnesium and its alloys

Alloying magnesium (Mg) with rare earth elements such as yttrium (Y) has been reported to activate the pyramidal slip systems and improve the plasticity of Mg at room temperature. However, the origins of such dislocations and their dissociation mechanisms remain poorly understood. Here, we systematically investigate these mechanisms using dispersion-inclusive density functional theory, in combination with molecular dynamics simulations. We find that dislocations form more readily on the pyramidal I plane than on the pyramidal II plane in Mg. The addition of Y atoms in Mg facilitates the dissociation of dislocations on pyramidal II, leading to the easier formation of the pyramidal II than pyramidal I in Mg-Y alloy. Importantly, in pyramidal II slip plane, a flat potential-energy surface (PES) exists around the position of stable stacking fault energy (SFE), which allows cooperative movement of atoms within the slip plane. Alloying Mg with Y atoms increases the range of the PES, and ultimately promotes different sliding pathways in the Mg-Y alloy. These findings are consistent with experimentally observed activation of the pyramidal II slip system in Mg-Y alloys, and provide important insight into the relationship between dislocation structure and macroscopic enhancement of plasticity.

cond-mat.mtrl-sci↗