Search Results for author:
Found 7 papers, 4 papers with code
Digging into Depth Priors for Outdoor Neural Radiance Fields
However, the shape-radiance ambiguity of radiance fields remains a challenge, especially in the sparse viewpoints setting.
Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others.
MapNeRF: Incorporating Map Priors into Neural Radiance Fields for Driving View Simulation
The key insight is that map information can be utilized as a prior to guiding the training of the radiance fields with uncertainty.
NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering
This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces using pre-integrated rendering from multi-view images or video.
Masked Representation Learning for Domain Generalized Stereo Matching
Experimental results on multi-datasets show that: (1) our method can be easily plugged into the current various stereo matching models to improve generalization performance; (2) our method can reduce the significant volatility of generalization performance among different training epochs; (3) we find that the current methods prefer to choose the best results among different training epochs as generalization performance, but it is impossible to select the best performance by ground truth in practice.
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching
In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network.
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
First, we construct combination volumes on the upper levels of the pyramid and develop a cost volume fusion module to integrate them for initial disparity estimation.
