arXiv AI
Computational Depth Measurement in Thermographic Video: Overcoming Spatial Overfitting via Spatio-Temporal Decoupling
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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
Sep 3
RAFT-DVC: Resolution-Aware Machine Learning-Based Digital Volume Correlation
RAFT-DVC is a resolution‑aware family of recurrent all‑pairs field transform (RAFT) based digital volume correlation (DVC) solvers that use encoder downsampling factors of 2, 4, and 8. The solvers localize displacement to about 0.017 feature‑grid voxels, with raw‑volume error scaling roughly as 0.017 s voxels, and exhibit complementary operating regimes determined by displacement reach and volumetric‑texture compatibility. Synthetic benchmarks show comparable performance to tuned classical DVC for fine‑texture, small‑to‑moderate displacements, while outperforming it for coarse‑texture, large‑displacement scenarios; additional tests on confocal and micro‑CT images confirm the importance of matching solver regimes to deformation magnitude and texture, and demonstrate cross‑texture transfer and improved accuracy after correcting sampler geometry.
arXiv Computer Vision
Sep 4
When Depth Hurts: Reliability-Aware Geometry Distillation for Depth-Free RGB-D Salient Object Detection
arXiv:2609. 03378v1 Announce Type: new Abstract: Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable.
arXiv Computer Vision
3d ago
ArtifactWorld: Scaling 3D Gaussian Splatting Artifact Restoration via Video Generation Models
arXiv:2604.12251v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) delivers high-fidelity real-time rendering but suffers from geometric and photometric degradations under sparse-view c...
arXiv Computer Vision
Sep 21
XCalib Depth-Guided Geometric Optimization for Dense Thermal-Visible Video Registration
XCalib is an unsupervised dense registration framework that aligns thermal and visible video streams by optimizing virtual pinhole camera parameters and predicted monocular depth, thereby restricting spatial displacements to physically valid projection geometries. It introduces a novel registration paradigm using camera parameterization as an implicit regularizer, a robust Normalized Edges Correlation (NEC) metric for cross‑spectral alignment, and demonstrates superior temporal stability and alignment accuracy on public ADAS datasets compared to unconstrained dense flow baselines.
arXiv AI
Jun 30
One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models
arXiv:2606. 29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid surfaces.