Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures
Abstract Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize
images of 3D scenes from novel views. However, they rely upon specialized volumetric …
images of 3D scenes from novel views. However, they rely upon specialized volumetric …
Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases
The proliferation of technologies, such as extended reality (XR), has increased the demand
for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications …
for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications …
Benchmarking neural radiance fields for autonomous robots: An overview
Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …
Learning a more continuous zero level set in unsigned distance fields through level set projection
Latest methods represent shapes with open surfaces using unsigned distance functions
(UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the …
(UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the …
Sparsepose: Sparse-view camera pose regression and refinement
Camera pose estimation is a key step in standard 3D reconstruction pipelines that operates
on a dense set of images of a single object or scene. However, methods for pose estimation …
on a dense set of images of a single object or scene. However, methods for pose estimation …
Gridpull: Towards scalability in learning implicit representations from 3d point clouds
Learning implicit representations has been a widely used solution for surface reconstruction
from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a …
from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a …
Canonical factors for hybrid neural fields
Factored feature volumes offer a simple way to build more compact, efficient, and
intepretable neural fields, but also introduce biases that are not necessarily beneficial for …
intepretable neural fields, but also introduce biases that are not necessarily beneficial for …
DReg-NeRF: Deep registration for neural radiance fields
Abstract Although Neural Radiance Fields (NeRF) is popular in the computer vision
community recently, registering multiple NeRFs has yet to gain much attention. Unlike the …
community recently, registering multiple NeRFs has yet to gain much attention. Unlike the …
Soac: Spatio-temporal overlap-aware multi-sensor calibration using neural radiance fields
In rapidly-evolving domains such as autonomous driving the use of multiple sensors with
different modalities is crucial to ensure high operational precision and stability. To correctly …
different modalities is crucial to ensure high operational precision and stability. To correctly …
Loopsplat: Loop closure by registering 3d gaussian splats
L Zhu, Y Li, E Sandström, S Huang, K Schindler… - ar** (SLAM) based on 3D Gaussian Splats (3DGS) has
recently shown promise towards more accurate, dense 3D scene maps. However, existing …
recently shown promise towards more accurate, dense 3D scene maps. However, existing …