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 …
Pie-nerf: Physics-based interactive elastodynamics with nerf
We show that physics-based simulations can be seamlessly integrated with NeRF to
generate high-quality elastodynamics of real-world objects. Unlike existing methods we …
generate high-quality elastodynamics of real-world objects. Unlike existing methods we …
Interactive nerf geometry editing with shape priors
Neural Radiance Fields (NeRFs) have shown great potential for tasks like novel view
synthesis of static 3D scenes. Since NeRFs are trained on a large number of input images, it …
synthesis of static 3D scenes. Since NeRFs are trained on a large number of input images, it …
Deformable model-driven neural rendering for high-fidelity 3D reconstruction of human heads under low-view settings
Reconstructing 3D human heads in low-view settings presents technical challenges, mainly
due to the pronounced risk of overfitting with limited views and high-frequency signals. To …
due to the pronounced risk of overfitting with limited views and high-frequency signals. To …
NeRF in Robotics: A Survey
Meticulous 3D environment representations have been a longstanding goal in computer
vision and robotics fields. The recent emergence of neural implicit representations has …
vision and robotics fields. The recent emergence of neural implicit representations has …
Efficient ray sampling for radiance fields reconstruction
S Sun, M Liu, Z Fan, Q Jiao, Y Liu, L Dong, L Kong - Computers & Graphics, 2024 - Elsevier
Accelerating the training process of neural radiance field holds substantial practical value.
The ray sampling strategy profoundly influences the convergence of this neural network …
The ray sampling strategy profoundly influences the convergence of this neural network …
Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs
The multi-plane representation has been highlighted for its fast training and inference across
static and dynamic neural radiance fields. This approach constructs relevant features via …
static and dynamic neural radiance fields. This approach constructs relevant features via …
Scene-Constrained Neural Radiance Fields for High-Quality Sports Scene Rendering Based on Visual Sensor Network
Y Dai, J Li, Y Zhang, Y Jiang, H Qin, X Zhou… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
Free-viewpoint videos offer audiences a more immersive and liberated way to watch sports.
The rendering of sports scenes encompasses two essential elements: dynamic targets and …
The rendering of sports scenes encompasses two essential elements: dynamic targets and …
Intensity Field Decomposition for Tissue-Guided Neural Tomography
MX Li, JG Yu, Y Gao, C Huang, GS Xia - arXiv preprint arXiv:2411.00900, 2024 - arxiv.org
Cone-beam computed tomography (CBCT) typically requires hundreds of X-ray projections,
which raises concerns about radiation exposure. While sparse-view reconstruction reduces …
which raises concerns about radiation exposure. While sparse-view reconstruction reduces …
Few-Shot Neural Radiance Fields under Unconstrained Illumination
In this paper, we introduce a new challenge for synthesizing novel view images in practical
environments with limited input multi-view images and varying lighting conditions. Neural …
environments with limited input multi-view images and varying lighting conditions. Neural …