Accurate 3D reconstruction of objects with reflective, transparent, or low-texture surfaces remains a significant challenge. Such materials often violate key assumptions in multi-view reconstruction pipelines, such as photometric consistency and the reliance on distinct geometric texture cues. Existing datasets primarily focus on diffuse, textured objects, thereby offering limited insight into performance under real-world material complexities. In this paper, we introduce 3DReflecNet, a large-scale hybrid dataset exceeding 22 TB that is specifically designed to benchmark and advance 3D vision methods for these challenging materials. 3DReflecNet combines two types of data: over 100,000 synthetic instances generated via physically-based rendering of more than 10,000 shapes, and over 1,000 real-world objects scanned using consumer RGB-D devices. Together, these data consist of more than 7 million multi-view frames. It encompasses diverse materials, complex lighting conditions, and a wide range of geometric forms—including shapes generated from both real and LLM-synthesized 2D images using diffusion-based methods. To support robust evaluation, we design benchmarks for four core tasks: image matching, reflection removal, structure-from-motion, and novel view synthesis. Through extensive experiments, we show that state-of-the-art methods struggle to maintain accuracy across these settings, highlighting the need for more resilient 3D vision models. We release the dataset, baselines, and evaluation suite to facilitate progress in this direction, which can be accessed at supplementary materials.

While 3D Gaussian splatting has emerged as a powerful paradigm, it fundamentally fails to model transparency such as glass panels, which are prevalent in everyday environments. The core challenge lies in decoupling the intertwined radiance contributions from transparent interfaces and the transmitted geometry observed through the glass. We present GLINT, a framework that models scene-scale transparency through explicit decomposed Gaussian representation. GLINT reconstructs the primary interface and separates outgoing radiance into reflection and transmission components according to its optical properties, enabling coherent Gaussian radiance transport. During the optimization, GLINT bootstraps transparency localization by utilizing geometry separation cues that emerge from our decomposition with the geometry and material priors from a pre-trained video relighting model. Extensive experiments demonstrate that GLINT achieves state-of-the-art performance in 3D reconstruction of complex transparent scenes.Our code will be released publicly.

The 3D characterization of microstructures is crucial for understanding and designing functional materials. However, the scanning electron microscope (SEM), widely used in scientific research, captures only 2D electron intensity distributions. Existing SEM 3D reconstruction methods struggle with textureless regions, shadowing artifacts, and calibration dependencies, whereas advanced learning-based approaches fail to generalize to microscopic SEM domains due to the lack of physical priors and domain-specific data. To address these challenges, we introduce NFH-SEM, a neural field-based hybrid reconstruction framework that recovers high-fidelity 3D surfaces from multi-view, multi-detector SEM images. NFH-SEM integrates coarse multi-view geometry with photometric stereo cues from detector signals through a continuous neural field, incorporating a learnable forward model that embeds SEM imaging physics for self-calibrated, shadow-robust reconstruction. NFH-SEM achieves precise recovery across diverse specimens, revealing 478 nm layered features in two-photon lithography samples, 782 nm surface textures on pollen grains, and 1.559 μm fracture steps on silicon carbide particles, demonstrating its accuracy and broad applicability.

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.

Multi-view 3D reconstruction methods remain highly sensitive to photometric inconsistencies arising from camera optical characteristics and variations in image signal processing (ISP). Existing mitigation strategies such as per-frame latent variables or affine color corrections lack physical grounding and generalize poorly to novel views. We propose the Physically-Plausible ISP (PPISP) correction module, which disentangles camera-intrinsic and capture-dependent effects through physically based and interpretable transformations. A dedicated PPISP controller, trained on the input views, predicts ISP parameters for novel viewpoints, analogous to auto exposure and auto white balance in real cameras. This design enables realistic and fair evaluation on novel views without access to ground-truth images. PPISP achieves SoTA performance on standard benchmarks, while providing intuitive control and supporting the integration of metadata when available.

Transparent objects are common in daily life, and understanding their multi-layer depth information, including both the transparent surface and the objects behind it, is crucial for real-world applications that interact with transparent materials.However, existing depth methods produce only a single depth map, which is inherently ambiguous for transparent surfaces.In this work, We propose a multi-layer depth estimation method, SeeGroup, consisting of novel recurrent decomposition module design and an intensity-based formulation for multi-layer depth. Experiments demonstrate that our method significantly improves the state of the art of multi-layer depth estimation, improving quadruplet relative depth accuracy on LayeredDepth benchmark from 61.34\% to 70.67\%.