Transparent objects are prevalent in various everyday scenarios. However, their reflective and refractive optical properties present significant challenges for conventional optical sensors. This difficulty makes the task of generating dense depth maps from sparse depth maps and high-resolution RGB images a critical area of research. In this paper, we introduce SRNet-Trans, a novel two-stage depth completion framework specifically designed for transparent objects. The approach is structured into two stages, each primarily focused on leveraging semantic and depth information, respectively. In the first stage, RGB images and sparse depth maps are used to predict a relatively dense depth map. The second stage then takes the predicted depth from the first stage, along with the sparse depth map, to generate a final dense depth map. The depth information produced by the two stages is complementary, allowing for effective fusion of both outputs. To enhance the depth estimation process, we integrate a self-attention mechanism in the first stage to better capture semantic features and introduce geometric convolutional layers in the second stage to improve depth encoding accuracy. Additionally, we incorporate a global consistency-based fine depth recovery technique to further refine the final depth map. Extensive experiments on the large-scale real-world TransCG dataset demonstrate that SRNet-Trans outperforms current state-of-the-art methods in terms of depth estimation accuracy.
Tao et al. (Tue,) studied this question.