ThermalGaussian: Thermal 3D Gaussian Splatting

Thermography is especially valuable for the military and other users of surveillance cameras. Some recent methods based on Neural Radiance Fields (NeRF) are proposed to reconstruct the thermal scenes in 3D from a set of thermal and RGB images. However, unlike NeRF, 3D Gaussian splatting (3DGS) prevails due to its rapid training and real-time rendering. In this work, we propose ThermalGaussian, the first thermal 3DGS approach capable of rendering high-quality images in RGB and thermal modalities. We first calibrate the RGB camera and the thermal camera to ensure that both modalities are accurately aligned. Subsequently, we use the registered images to learn the multimodal 3D Gaussians. To prevent the overfitting of any single modality, we introduce several multimodal regularization constraints. We also develop smoothing constraints tailored to the physical characteristics of the thermal modality. Besides, we contribute a real-world dataset named RGBT-Scenes, captured by a hand-hold thermal-infrared camera, facilitating future research on thermal scene reconstruction. We conduct comprehensive experiments to show that ThermalGaussian achieves photorealistic rendering of thermal images and improves the rendering quality of RGB images. With the proposed multimodal regularization constraints, we also reduced the model's storage cost by 90\%. The code and dataset will be released.

SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization

Cross-view geo-localization aims to match images of the same target from different platforms, e.g., drone and satellite. It is a challenging task due to the changing both appearance of targets and environmental content from different views. Existing methods mainly focus on digging more comprehensive information through feature maps segmentation, while inevitably destroy the image structure and are sensitive to the shifting and scale of the target in the query. To address the above issues, we introduce a simple yet effective part-based representation learning, called shifting-dense partition learning (SDPL). Specifically, we propose the dense partition strategy (DPS), which divides the image into multiple parts to explore contextual-information while explicitly maintain the global structure. To handle scenarios with non-centered targets, we further propose the shifting-fusion strategy, which generates multiple sets of parts in parallel based on various segmentation centers and then adaptively fuses all features to select the best partitions. Extensive experiments show that our SDPL is robust to position shifting and scale variations, and achieves competitive performance on two prevailing benchmarks, i.e., University-1652 and SUES-200.