We present MosaicMVS, a novel learning-based depth estimation framework for a mosaic-based omnidirectional multi-view stereo (MVS) camera setup. It uses a regular field of view (FOV) MVS network for an omnidirectional imaging setup with explicit consideration of hypothetical voxel-wise FOV overlaps. The resulting depth predictions are accurate and agree on the omnidirectional multi-view geometry. Unlike existing MVS setups, MosaicMVS camera setup can be easily applied to omnidirectional indoor scenes without having to account for constraints such as intricate epipolar constraints and the distortion of omnidirectional cameras. We validate the effectiveness of our framework on a new challenging indoor dataset in terms of depth estimation, reconstruction, and view synthesis. We also present new evaluation metric to check reconstruction performance using post-processed masks for accurate evaluation without any ground truth depth map or laser-scanned reconstructions. Experimental results show that our framework outperforms the state-of-the-art MVS methods in a large margin in all test scenes.

In the field of video depth estimation, significant strides have been made with deep learning-based multi-view stereo approaches. However, existing studies struggle to produce consistently accurate depth maps that account for both multi-view geometry and temporal consistency from monocular video contents. To overcome this limitation, we introduce CMVDE, an innovative video depth estimation framework that leverages a multi-view geometric-temporal coupling approach in an end-to-end manner. Our proposed geometric consistency module efficiently generates multi-view geometric features by employing mutual cross-view epipolar attention between adjacent video frames. Additionally, it compresses these features using the novel multi-scale feature compressor, producing an effective input tensor for the subsequent module. Moreover, our framework enhances temporal consistency across consecutive video frames with the temporal consistency module based on convolutional LSTM 1 leveraging previous depth information as geometric guidance. Compared to state-of-the-art models, our approach achieves superior performance in depth quality and consecutive consistency on the ScanNet 2 and 7-Scenes 3 datasets, surpassing previous multi-view video depth estimation methods.