← 研踪 PaperTrailarXiv:2611.15435 · v1
ThermalSplat: Generalizable Pose-Free Gaussian Splatting for Sparse-View Thermal 3D Reconstruction
首次提交 2026-11-11 21:09
版本更新 2026-11-11 21:09
雷达观测 2026-11-13 11:11
摘要
Thermal infrared imaging is vital for applications like search-and-rescue and autonomous navigation, yet trans- lating it into 3D scene representations remains a critical bottleneck. The low-texture nature and view-dependent emissivity of thermal images cause standard pose estima- tors to fail catastrophically, collapsing standard 3D Gaus- sian Splatting pipelines under sparse-view constraints. Fur- thermore, existing thermal reconstruction methods rely heavily on dense capture and computationally expensive per-scene optimization. Critically, many of these ap- proaches depend on aligned RGB-thermal datasets to ob- tain reliable camera poses and initial point clouds, ren- dering them unusable in purely thermal, unposed envi- ronments. To break this bottleneck, we present Thermal- Splat, the first generalizable, feed-forward Gaussian splat- ting framework that reconstructs 3D scenes directly from sparse thermal images without requiring known camera poses, RGB guidance, or per-scene training. We over- come thermal-specific challenges using a soft census loss for brightness-invariant structural matching and a bound- ary structure loss to explicitly supervise temperature discon- tinuities. Trained on an aggregated multi-source dataset, ThermalSplat synthesizes sharp reconstructions of unseen scenes in a single feed-forward pass. Extensive experi- ments demonstrate that ThermalSplat holds a strict advan- tage over scene-specific thermal splatting methods in the sparse regime. Furthermore, even compared against these baselines operating under their ideal dense capture set- tings, ThermalSplat achieves superior perceptual fidelity. By eliminating the need for computationally expensive per scene optimization, ThermalSplat offers an efficient and generalizable approach to thermal 3D reconstruction.
作者备注：WACV 2027
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-11 21:09 · 最新观测版本
ThermalSplat: Generalizable Pose-Free Gaussian Splatting for Sparse-View Thermal 3D Reconstruction
Thermal infrared imaging is vital for applications like search-and-rescue and autonomous navigation, yet trans- lating it into 3D scene representations remains a critical bottleneck. The low-texture nature and view-dependent emissivity of thermal images cause standard pose estima- tors to fail catastrophically, collapsing standard 3D Gaus- sian Splatting pipelines under sparse-view constraints. Fur- thermore, existing thermal reconstruction methods rely heavily on dense capture and computationally expensive per-scene optimization. Critically, many of these ap- proaches depend on aligned RGB-thermal datasets to ob- tain reliable camera poses and initial point clouds, ren- dering them unusable in purely thermal, unposed envi- ronments. To break this bottleneck, we present Thermal- Splat, the first generalizable, feed-forward Gaussian splat- ting framework that reconstructs 3D scenes directly from sparse thermal images without requiring known camera poses, RGB guidance, or per-scene training. We over- come thermal-specific challenges using a soft census loss for brightness-invariant structural matching and a bound- ary structure loss to explicitly supervise temperature discon- tinuities. Trained on an aggregated multi-source dataset, ThermalSplat synthesizes sharp reconstructions of unseen scenes in a single feed-forward pass. Extensive experi- ments demonstrate that ThermalSplat holds a strict advan- tage over scene-specific thermal splatting methods in the sparse regime. Furthermore, even compared against these baselines operating under their ideal dense capture set- tings, ThermalSplat achieves superior perceptual fidelity. By eliminating the need for computationally expensive per scene optimization, ThermalSplat offers an efficient and generalizable approach to thermal 3D reconstruction.
查看此版本解读 →查看此版本原文 ↗