To obtain reliable depth images with high resolution, a novel method is proposed in this study that fuses data acquired from time-of-flight (ToF) and stereo cameras, through which the advantages of both active and passive sensing are utilised. Based on the classic error model of the ToF, gradient information is introduced to establish the likelihood distribution for all disparity candidates. The stereo likelihood is estimated in parallel based on a 3D adaptive support-weight approach. The two independent likelihoods are unified using a maximum likelihood estimation, a process also referred to as a joint depth filter herein. Conventional post-processing methods such as a mutual consistency check are also used after applying a joint depth filter. We also propose a novel hole-filling method based on the seed-growing algorithm to retrieve missing disparities. Experiment results show that the proposed fusion method can produce reliable high-resolution depth maps and outperforms other compared methods.

为了获得高分辨率的可靠深度图像，本研究提出了一种新颖的方法，该方法融合了从飞行时间（ToF）和立体相机获取的数据，从而利用了主动和被动感应的优势。基于ToF的经典误差模型，引入了梯度信息以建立所有视差候选者的似然分布。基于3D自适应支持权重方法并行估计立体声可能性。使用最大似然估计来统一这两个独立的似然，该过程在本文中也称为联合深度滤波器。在应用联合深度过滤器之后，还使用了诸如相互一致性检查之类的常规后处理方法。我们还提出了一种基于种子生长算法的新颖的填充孔的方法来检索丢失的视差。实验结果表明，所提出的融合方法能够生成可靠的高分辨率深度图，并且优于其他比较方法。