3DReflecNet：面向反射、透明与低纹理物体三维重建的大规模数据集
3DReflecNet: A Large-Scale Dataset for 3D Reconstruction of Reflective, Transparent, and Low-Texture Objects
该数据集为反射/透明/低纹理物体的多视图重建提供了大规模合成与真实数据，并定义了图像匹配、SfM、新视角合成、反射去除和重光照五个基准任务，可直接用于评测和开发针对这些挑战性材料的鲁棒3D视觉方法。
原文依据 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.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论对具有反射、透明或低纹理表面的物体进行精确三维重建仍然是一项众所周知的挑战。此类材料常常违反多视图重建流程中的关键假设，例如光度一致性以及可用的清晰几何纹理线索。现有数据集主要关注漫反射、有纹理的物体，因此对真实世界材料复杂性下的性能洞察有限。我们提出了 3DReflecNet，一个超过 22 TB 的大规模混合数据集，专门用于基准测试和推进面向这些挑战性材料的三维视觉方法。3DReflecNet 结合了两类数据：通过基于物理的渲染对超过 12,000 个形状生成的超过 120,000 个合成实例，以及使用消费级设备捕获的超过 1,000 个真实世界物体。这些数据合计包含超过 700 万张多视图帧。该数据集涵盖多样化的材料、复杂的光照条件以及广泛的几何形态——包括使用基于扩散的流程从真实和 LLM 合成的二维图像生成的形状。为支持稳健评估，我们为五个核心任务设计了基准：图像匹配、运动恢复结构、新视角合成、反射去除和重光照。大量实验表明，最优方法在这些设置下难以保持准确性，凸显了对更具韧性的三维视觉模型的需求。
英文原文摘要 1473 字符
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.