5 accepted papers
Cascade-based multi-scale architectures are currently the mainstream in Multi-view Stereo (MVS), achieving a balance between computational efficiency and reconstruction accuracy. However, existing cascade MVS methods suffer from significant limitations in cross-scale information utilization, where d
Learning-based Multi-View Stereo (MVS) methods have become the mainstream in the field, relying on the construction of cost Learning-based Multi-View Stereo (MVS) methods have become the mainstream in the field, relying on the construction of cost volumes through multi-view feature similarity comput
Despite the promising performance achieved by the learning-based multi-view stereo (MVS) methods, the commonly used feature extractors still struggle with the perspective transformation across different viewpoints. Furthermore, existing methods generally employ a “one-to-many” strategy, computing th
The encoder-decoder network (ED-Net) is a commonly employed choice for existing depth completion methods but its working mechanism is ambiguous. In this paper we visualize the internal feature maps to analyze how the network densifies the input sparse depth. We find that the encoder feature of ED-Ne…
In this paper, we propose a novel iterative dynamic cost volume for multi-view stereo. Compared with other works, our cost volume is much lighter, thus could be processed with 2D convolution based GRU. Notably, the every-step output of the GRU could be further used to generate new cost volume. In th…