DCT-Diffusion: Depth Completion for Transparent Objects with Diffusion Denoising Approach

Abstract

Transparent objects are common in industrial automation and daily life. However, accurate visual perception of these objects remains challenging due to their reflective and refractive properties. Most previous studies fail to capture contextual information or typically rely on regression-based methods at the decoder stage, suffering from overfitting and unsatisfactory object details. To overcome these limitations, we present a novel depth completion framework for transparent objects with diffusion denoising approach (DCT-Diffusion). First, we adopt a transformer-based encoder to globally learn the depth relationships from different parts of the input by modeling long-distance dependencies. Then, we propose to introduce the diffusion model to generate refined depth maps from random depth distribution. Through iterative refinement, our model can progressively enhance depth map details and achieves fine-grained performance. Lastly, a conditioned fusion module is developed, which utilizes encoder features as visual conditions and fuses them with the denoising block at each step using augmented attention. Extensive comparative studies and cross-domain experiments prove that the DCT-Diffusion outperforms previous methods and significantly improves the robustness and generalization ability. Moreover, visualization results further illustrate that our method can generate depth maps with more complete geometry and clearer boundaries, achieving satisfactory results.