5 accepted papers
We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for additional networks, making it particularly suited for challenging observations and uncontrolled image environments. NeRF d…
Implicit Neural Representation (INR)-based SLAM has a critical issue where all keyframes must be stored in memory for post-training whenever a remapping is needed due to the neural network's weights themselves representing the map. To address this, previous INR-based SLAM proposed methods to modify …
We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for additional networks, making it particularly suited for challenging observations and uncontrolled image environments. NeRF d
Implicit Neural Representation (INR)-based SLAM has a critical issue where all keyframes must be stored in memory for post-training whenever a remapping is needed due to the neural network's weights themselves representing the map. To address this, previous INR-based SLAM proposed methods to modify
With the advent of Neural Radiance Field (NeRF), representing 3D scenes through multiple observations has shown significant improvements. Since this cutting-edge technique can obtain high-resolution renderings by interpolating dense 3D environments, various approaches have been proposed to apply NeR…