Our proposed EVE-NeRF harnesses both cross-view and along-epipolar information in an entangled manner to address limitations in existing strategies, resulting in state-of-the-art performance.
NeRF-VPTは、新しい視点表現を学習するための革新的な手法であり、NeRFに基づくアプローチと比較して高品質な画像を生成します。
Developing a general-purpose NeRF model for versatile 3D tasks.
ThermoNeRF proposes a multimodal approach using Neural Radiance Fields for accurate thermal image synthesis and reconstruction.
EVE-NeRF harnesses cross-view and along-epipolar information to enhance 3D representation.
Leveraging uncertainty in depth priors through Earth Mover's Distance improves NeRF training.
CombiNeRF combines multiple regularization techniques to improve few-shot neural radiance field view synthesis.
CombiNeRF combines various regularization techniques to enhance few-shot neural radiance field view synthesis.
GS-W introduces 3D Gaussian points with separated intrinsic and dynamic appearance features to improve scene reconstruction quality and rendering speed.
Proposing a novel approach to improve NeRF's performance with sparse inputs by modeling 3D spatial field consistency.