Fei Wang

Zheng Dang, Jialu Huang, Fei Wang et al.

Recent advances in deep learning, such as neural radiance fields and implicit neural representations, have significantly advanced 3D reconstruction. However, accurately reconstructing objects with complex optical properties, such as metals, glass, and plastics, remains challenging due to the breakdown of multi-view color consistency in the presence of specular reflections, refractions, and transparency. This limitation is further exacerbated by the lack of benchmark datasets that explicitly model material-dependent light transport. To address this, we introduce OpenMaterial, a large-scale semi-synthetic dataset for benchmarking material-aware 3D reconstruction. It comprises 1,001 objects spanning 295 distinct materials, including conductors, dielectrics, plastics, and their roughened variants, captured under 714 diverse lighting conditions. By integrating lab-measured Index of Refraction (IOR) spectra, OpenMaterial enables the generation of high-fidelity multi-view images that accurately simulate complex light-matter interactions. It provides multi-view images, 3D shape models, camera poses, depth maps, and object masks, establishing the first extensive benchmark for evaluating 3D reconstruction on challenging materials. We evaluate 11 state-of-the-art methods for 3D reconstruction and novel view synthesis, conducting ablation studies to assess the impact of material type, shape complexity, and illumination on reconstruction performance. Our results indicate that OpenMaterial provides a strong and fair basis for developing more robust, physically-informed 3D reconstruction techniques to better handle real-world optical complexities.

Marianne Arriola, Weishen Pan, Manqi Zhou et al.

Joint analysis of multi-omic single-cell data across cohorts has significantly enhanced the comprehensive analysis of cellular processes. However, most of the existing approaches for this purpose require access to samples with complete modality availability, which is impractical in many real-world scenarios. In this paper, we propose (Single-Cell Cross-Cohort Cross-Category) integration, a novel framework that learns unified cell representations under domain shift without requiring full-modality reference samples. Our generative approach learns rich cross-modal and cross-domain relationships that enable imputation of these missing modalities. Through experiments on real-world multi-omic datasets, we demonstrate that offers a robust solution to single-cell tasks such as cell type clustering, cell type classification, and feature imputation.