We present LoD-NeuS, an efficient neural representation for high-frequency geometry detail
recovery and anti-aliased novel view rendering. Drawing inspiration from voxel-based …

Abstract Neural Radiance Fields (NeRFs) can be dramatically accelerated by spatial grid
representations. However, they do not explicitly reason about scale and so introduce …

Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …

Generalizable NeRF can directly synthesize novel views across new scenes eliminating the
need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these …

Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …

Recent advances in implicit neural representation have demonstrated the ability to recover
detailed geometry and material from multi-view images. However, the use of simplified …

The use of Implicit Neural Representation (INR) through a hash-table has demonstrated
impressive effectiveness and efficiency in characterizing intricate signals. However, current …

Neural implicit fields have been a de facto standard in novel view synthesis. Recently there
exist some methods exploring fusing multiple modalities within a single field aiming to share …

Meticulous 3D environment representations have been a longstanding goal in computer
vision and robotics fields. The recent emergence of neural implicit representations has …

Recent advances in implicit neural representation have demonstrated the ability to recover
detailed geometry and material from multi-view images. However, the use of simplified …