Fieldscale: Locality-Aware Field-based Adaptive Rescaling for Thermal Infrared Image

Thermal infrared (TIR) cameras are emerging as promising sensors in safety-related fields due to their robustness against external illumination. However, RAW TIR image has 14 bits of pixel depth and needs to be rescaled into 8 bits for general applications. Previous works utilize a global 1D look-up table to compute pixel-wise gain solely based on its intensity, which degrades image quality by failing to consider the local nature of the heat. We propose Fieldscale, a rescaling based on locality-aware 2D fields where both the intensity value and spatial context of each pixel within an image are embedded. It can adaptively determine the pixel gain for each region and produce spatially consistent 8-bit rescaled images with minimal information loss and high visibility. Consistent performance improvement on image quality assessment and two other downstream tasks support the effectiveness and usability of Fieldscale. All the codes are publicly opened to facilitate research advancements in this field. https://github.com/hyeonjaegil/fieldscale

HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatial and Temporal Variations

Place recognition is crucial for robotic localization and loop closure in simultaneous localization and mapping (SLAM). Recently, LiDARs have gained popularity due to their robust sensing capability and measurement consistency, even in the illumination-variant environment, offering an advantage over traditional imaging sensors. Spinning LiDARs are widely accepted among many types, while non-repetitive scanning patterns have recently been utilized in robotic applications. Beyond the range measurements, some LiDARs offer additional measurements, such as reflectivity, Near Infrared (NIR), and velocity (e.g., FMCW LiDARs). Despite these advancements, a noticeable dearth of datasets comprehensively reflects the broad spectrum of LiDAR configurations optimized for place recognition. To tackle this issue, our paper proposes the HeLiPR dataset, curated especially for place recognition with heterogeneous LiDAR systems, embodying spatial-temporal variations. To the best of our knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset designed to support inter-LiDAR place recognition with both non-repetitive and spinning LiDARs, accommodating different field of view (FOV) and varying numbers of rays. Encompassing the distinct LiDAR configurations, it captures varied environments ranging from urban cityscapes to high-dynamic freeways over a month, designed to enhance the adaptability and robustness of place recognition across diverse scenarios. Notably, the HeLiPR dataset also includes trajectories that parallel sequences from MulRan, underscoring its utility for research in heterogeneous LiDAR place recognition and long-term studies. The dataset is accessible at https: //sites.google.com/view/heliprdataset.

MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks

In this study, a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. Our proposed network consists of a multi-stage encoder-decoder architecture and Cross Stage Feature Aggregation (CSFA). The proposed multi-stage encoder-decoder architecture alleviates the partial observation problem caused by the characteristics of a 2D LiDAR, and CSFA prevents the multi-stage network from diluting the features and allows the network to learn the inter-spatial relationship between features better. Previous works use sub-sampled data from the ground truth as an input rather than actual 2D LiDAR data. In contrast, our approach trains the model and conducts experiments with a physically-collected 2D LiDAR dataset. To this end, we acquired our own dataset called KAIST RGBD-scan dataset and validated the effectiveness and the robustness of MSDPN under realistic conditions. As verified experimentally, our network yields promising performance against state-of-the-art methods. Additionally, we analyzed the performance of different input methods and confirmed that the reference depth map is robust in untrained scenarios.