Multi-view stereo infers the 3D geometry from a set of images captured from several known
positions and viewpoints. It is one of the most important components of 3D reconstruction …

We present a method that synthesizes novel views of complex scenes by interpolating a
sparse set of nearby views. The core of our method is a network architecture that includes a …

In this paper, we present TransMVSNet, based on our exploration of feature matching in
multi-view stereo (MVS). We analogize MVS back to its nature of a feature matching task and …

In this paper, we present a novel recurrent multi-view stereo network based on long short-
term memory (LSTM) with adaptive aggregation, namely AA-RMVSNet. We firstly introduce …

Multi-frame depth estimation improves over single-frame approaches by also leveraging
geometric relationships between images via feature matching, in addition to learning …

Abstract Recent cascade Multi-View Stereo (MVS) methods can efficiently estimate high-
resolution depth maps through narrowing hypothesis ranges. However, previous methods …

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost
volumes. Due to the high computation and memory consumption of 3D CNN, the resolution …

We introduce DoubleField, a novel framework combining the merits of both surface field and
radiance field for high-fidelity human reconstruction and rendering. Within DoubleField, the …

We propose DeepMultiCap, a novel method for multi-person performance capture using
sparse multi-view cameras. Our method can capture time varying surface details without the …

In recent years, deep learning-based approaches have shown great strength in multi-view
stereo because of their outstanding ability to extract robust visual features. However, most …