arXiv Computer Vision
Dynamic Thermal Gaussians: Multimodal 4D Gaussian Splatting
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Sep 21
Dynamic Thermal Gaussians: Multimodal 4D Gaussian Splatting
Thermography plays a vital role in military and broader thermal analysis applications. Recent progress in 3D thermal reconstruction has extended temperature analysis from 2D to 3D space, yet most exis...
arXiv Computer Vision
Sep 1
ThermoSplat: Cross-Modal 3D Gaussian Splatting with Feature Modulation and Geometry Decoupling
arXiv:2601.15897v3 Announce Type: replace Abstract: Multi-modal scene reconstruction integrating RGB and thermal infrared data is essential for robust environmental perception across diverse lighting...
arXiv AI
Jul 10
Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations
arXiv:2607. 07962v1 Announce Type: cross Abstract: Inferring latent physical properties from sensory observations is a fundamental challenge in machine perception.
arXiv Computer Vision
3d ago
EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
arXiv:2601.15951v2 Announce Type: replace Abstract: Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle t...
Hugging Face Trending Papers
Jul 20
AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation
Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive.
arXiv AI
Sep 4
TruncGradGS: Improved 3D Gaussian Splatting via Truncated Gradient Updates
The paper introduces TruncGradGS, a piecewise truncated gradient approach that mitigates gradient vanishing in 3D Gaussian Splatting, enhancing optimization stability and robustness to initializations. It demonstrates consistent improvements over random and COLMAP initializations in both static and dynamic settings. Additionally, the authors highlight limitations of existing dynamic scene benchmarks and present a new synthetic dataset for evaluating dynamic Gaussian Splatting.