Search Results for author:
Found 8 papers, 3 papers with code
Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction
Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules.
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation
To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain.
RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo
At the pixel level, we propose to break the symmetry of the Siamese network (which is typically used in MVS to extract image features) by introducing a transformer block to the reference image (but not to the source images).
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
The semantic plane detection branch is based on a single-view plane detection framework but with differences.
Do End-to-end Stereo Algorithms Under-utilize Information?
In this paper, we show how deep adaptive filtering and differentiable semi-global aggregation can be integrated in existing 2D and 3D convolutional networks for end-to-end stereo matching, leading to improved accuracy.
Matching-space Stereo Networks for Cross-domain Generalization
End-to-end deep networks represent the state of the art for stereo matching.
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions.
