10 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…
We propose GSO-SLAM, a real-time monocular dense SLAM system that leverages Gaussian scene representation. Unlike existing methods that couple tracking and mapping with a unified scene, incurring computational costs, or loosely integrate them with well-structured tracking frameworks, introducing red
In this paper, we introduce a novel approach for modeling a memory-efficient spatial representation with 3D Gaussian splatting. Efficient vision-based spatial representation poses a significant challenge due to the memory demands of visual information. Recent advances in 3D rendering technologies, s
In this paper, we introduce a novel approach for modeling a memory-efficient spatial representation with 3D Gaussian splatting. Efficient vision-based spatial representation poses a significant challenge due to the memory demands of visual information. Recent advances in 3D rendering technologies, s…
Path planning in large-scale, complex 3D environments is fundamentally constrained by a trade-off between path quality and computational speed. This paper presents RUSH (Recursive and Scalable 3D <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Coarse
Path planning in large-scale, complex 3D environments is fundamentally constrained by a trade-off between path quality and computational speed. This paper presents RUSH (Recursive and Scalable 3D Coarse To Fine Path Planning), a hierarchical framework that resolves this trade-off. RUSH decomposes th…
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
"Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications. Recent advancements in dense representation SLAM have highlighted the potential of leveraging neural scene representation and 3D Gaussi…