Accurate 3D reconstruction of objects with reflective, transparent, or low-texture surfaces still remains notoriously challenging. Such materials often violate key assumptions in multi-view reconstruction pipelines, such as photometric consistency and the availability on distinct geometric texture cues. Existing datasets primarily focus on diffuse, textured objects, and therefore provide limited insight into performance under real-world material complexities. 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 120,000 synthetic instances generated via physically-based rendering of more than 12,000 shapes, and over 1,000 real-world objects captured using consumer devices. Together, these data consist of more than 7 million multi-view frames. The dataset spans 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 pipelines. To support robust evaluation, we design benchmarks for five core tasks: image matching, structure-from-motion, novel view synthesis, reflection removal, and relighting. Extensive experiments demonstrate that state-of-the-art methods struggle to maintain accuracy across these settings, highlighting the need for more resilient 3D vision models.

翻译：反射、透明或低纹理表面的物体精确三维重建仍面临显著挑战。这类材质常违反多视图重建流水线中的关键假设，如光度一致性和明确几何纹理线索的可用性。现有数据集主要聚焦漫反射、有纹理物体，因此对真实世界材质复杂性下的重建性能认知有限。我们提出3DReflecNet——一个超过22 TB的大规模混合数据集，专为基准测试并推动针对这些挑战性材质的3D视觉方法而设计。3DReflecNet融合两类数据：基于物理渲染技术对超过12,000个形状生成的逾120,000个合成实例，以及使用消费级设备采集的1,000余个真实物体。二者合计包含超过700万帧多视图图像。数据集涵盖多样化材质、复杂光照条件及广泛几何形态，其中包括由真实图像和基于扩散流水线的LLM合成二维图像生成的形状。为支撑稳健评估，我们设计了五项核心任务基准：图像匹配、运动恢复结构、新视角合成、反射移除及重光照。大量实验表明，当前最先进方法在此类场景中难以保持精度，凸显了开发更具鲁棒性的3D视觉模型的迫切性。