Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior
approaches build on NeRF and rely on implicit representations. This is slow since it requires …

Neural surface reconstruction has been shown to be powerful for recovering dense 3D
surfaces via image-based neural rendering. However, current methods struggle to recover …

We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The
key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene …

Neural implicit representations have recently demonstrated compelling results on dense
Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors …

We present ESLAM, an efficient implicit neural representation method for Simultaneous
Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera …

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video
data. The learnt avatar not only provides expressive control of the body, hands and the face …

This paper aims to tackle the challenge of efficiently producing interactive free-viewpoint
videos. Some recent works equip neural radiance fields with image encoders, enabling …

We present a novel multi-view implicit surface reconstruction technique, termed StreetSurf,
that is readily applicable to street view images in widely-used autonomous driving datasets …

High-quality large-scale scene rendering requires a scalable representation and accurate
camera poses. This research combines tile-based hybrid neural fields with parallel …

Existing neural implicit surface reconstruction methods have achieved impressive
performance in multi-view 3D reconstruction by leveraging explicit geometry priors such as …