Transparent object reconstruction in an uncontrolled natural scene is a challenging task due to its complex appearance. Existing methods optimize the object shape with RGB color as supervision, which suffer from locality and ambiguity, and fail to recover accurate structures. In this paper, we present RCTrans, which uses ray-background intersection as a more efficient constraint to achieve high-quality reconstruction, while maintaining a convenient setup. The key technology to achieve this is a novel pre-trained correspondence estimation network, which allows us to acquire ray-background correspondence under uncontrolled scenes and camera views. In addition, a confidence evaluation is introduced to protect the reconstruction from inaccurate estimated correspondence. Extensive experiments on both synthetic and real data demonstrate that our method can produce highly accurate results, without any extra acquisition burden. The code and dataset will be publicly available.

Recovering spatial-varying bi-directional reflectance distribution function (SVBRDF) from a few hand-held captured images has been a challenging task in computer graphics. Benefiting from the learned priors from data, single-image methods can obtain plausible SVBRDF estimation results. However, the extremely limited appearance information in a single image does not suffice for high-quality SVBRDF reconstruction. Although increasing the number of inputs can improve the reconstruction quality, it also affects the efficiency of real data capture and adds significant computational burdens. Therefore, the key challenge is to minimize the required number of inputs, while keeping high-quality results. To address this, we propose maximizing the effective information in each input through a novel co-located capture strategy that combines near-field and far-field point lighting. To further enhance effectiveness, we theoretically investigate the inherent relation between two images. The extracted relation is strongly correlated with the slope of specular reflectance, substantially enhancing the precision of roughness map estimation. Additionally, we designed the registration and denoising modules to meet the practical requirements of hand-held capture. Quantitative assessments and qualitative analysis have demonstrated that our method achieves superior SVBRDF estimations compared to previous approaches. All source codes will be publicly released.