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Found 5 papers, 3 papers with code
MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo
Recent advancements in learning-based Multi-View Stereo (MVS) methods have prominently featured transformer-based models with attention mechanisms.
Ranked #1 on
Point Clouds
on Tanks and Temples
Rethinking the Multi-view Stereo from the Perspective of Rendering-based Augmentation
To address these problems, we first apply one of the state-of-the-art learning-based MVS methods, --MVSFormer, to overcome intractable scenarios such as textureless and reflections regions suffered by traditional PatchMatch methods, but it fails in a few large scenes' reconstructions.
LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling
Recent progress in 4D implicit representation focuses on globally controlling the shape and motion with low dimensional latent vectors, which is prone to missing surface details and accumulating tracking error.
MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth
In this paper, we propose a pre-trained ViT enhanced MVS network called MVSFormer, which can learn more reliable feature representations benefited by informative priors from ViT.
Ranked #2 on
3D Reconstruction
on DTU
Density-preserving Deep Point Cloud Compression
To address this, we propose a novel deep point cloud compression method that preserves local density information.
