Single-photon lidar devices can acquire 3D data at very long range with high precision. Moreover, recent advances in lidar arrays have enabled acquisitions at very high frame rates. However, these devices place a severe bottleneck on the reconstruction algorithms, which have to handle very large volumes of noisy data. Recently, real-time 3D reconstruction of distributed surfaces has been demonstrated obtaining information at one wavelength. Here, we propose a new algorithm that achieves color 3D reconstruction without increasing the execution time nor the acquisition process of the realtime single-wavelength reconstruction system. The algorithm uses a coded aperture that compresses the data by considering a subset of the wavelengths per pixel. The reconstruction algorithm is based on a plug-and-play denoising framework, which benefits from off-the-shelf point cloud and image denoisers. Experiments using real lidar data show the competitivity of the proposed method.