We present our work on cell supervised 360-depth estimation based on spherical view synthesis. Data-driven depth perception needs to overcome the challenge of high-quality data collection. While low-cost sensors provide an easy means to create datasets, these suffer from systematic measurement noise and artifacts like holes. Synthetic data generation is another option, which nonetheless needs to overcome the evident domain gap. As a result, indirect approaches surfaced using view synthesis, exploiting the easy availability of stereo data or even unstructured videos. These cell supervised approaches rely on view synthesis supervision via inverse warping. For spherical images, there is a lack of stereo datasets, and a full disparity model has not been defined. We overcome the difficulty in collecting 360 stereo data by generating a dataset through ray tracing 3D scenes and produce renders for two different 360 stereo setups. We additionally derive the complete spherical disparity model based on Cartesian displaced viewpoint by taking the analytic derivatives of the spherical coordinates with respect to the Cartesian ones, the spherical disparity gamma is expressed as a function of depth and the displacement vector, or otherwise the baseline B. We can then arrive to a disparity definition for traditional single axis baselines. We observe that there is no horizontal disparity on the equirect angular image for the vertical placement, while both disparity directions are needed for horizontally displaced viewpoints. For the simpler vertical stereo, view synthesis else supervision can be achieved using inverse warping. However, horizontal stereo is more challenging due to the irregular disparity patterns, and the singularities where disparity approaches zero, making gradients inconsistent. Sphereical distortion also produces one to many pixel mappings, as well as many to one pixel mappings. Consequently, learning can't be achieved within inverse warping, necessitating the use of splitting. Our architecture uses a large receptive field with pre-activated residual blocks and exponential linear unit activations. Through the estimated depth we synthesize the other stereo views. To reduce the effect of inconsistent gradient flows and regularize the effect of distortion, we employ an attention mechanism. We supervise our model with a reconstruction loss and a total variation smoothness prior. Moreover, our model uses coordinate convolutions to allow implicit learning of the equirect angular context. We also investigate the complementarity of a translocular stereo setup using a blending factor. Interestingly we find that there exist blending factors that deteriorate performance. While vertical stereo offers good performance, we manage to successfully train a horizontal model. Our ablation analysis reveals the coordinate convolutions greatly improved the performance in the vertical case, while spherical attention considerably improved the performance of the horizontal one. Finally, we are in the unique position to compare cell supervision to fully supervised approaches. Our results showcase the quantitative superiority of the fully supervised model. Nonetheless, cell supervised models produce smoother results and qualitatively better write-view reconstruction results, including, we train to 360-depth estimation model using view synthesis in a horizontal stereo setup, something that was not possible with traditional warping and without a full spherical disparity model. Our work can enable future research for cell supervised learning using unstructured 360 videos, but also raises an open question whether this is the right direction.