arXiv:2604.26368v1 Announce Type: new
Abstract: In several applications it is desired to have 3D models not only from the outdoor spaces but also from inside the building. In the context of First Responder enhancement in large scale natural and man-ma…
arXiv:2604.26566v1 Announce Type: cross
Abstract: Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared charging infrastruct…
arXiv:2603.01999v2 Announce Type: replace-cross
Abstract: Reliable obstacle avoidance in industrial settings demands 3D scene understanding, but widely used 2D LiDAR sensors perceive only a single horizontal slice of the environment, missing critical …
arXiv:2604.26379v1 Announce Type: new
Abstract: Reliable seizure detection in mouse models is essential for preclinical epilepsy research, yet manual review of synchronized video-EEG recordings is labor-intensive and single-modality systems fail for c…
arXiv:2603.19199v2 Announce Type: replace-cross
Abstract: Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world. Existing asynchronous inference methods primarily optimize trajectory smoothness, but neg…
arXiv:2604.25942v1 Announce Type: new
Abstract: Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machine-learning framework t…
arXiv:2604.26031v1 Announce Type: new
Abstract: This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-t…
arXiv:2604.26133v1 Announce Type: new
Abstract: Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement characteristics with environme…
arXiv:2604.25936v1 Announce Type: cross
Abstract: Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost of network evaluations. We observe that implicit representat…
arXiv:2604.26181v1 Announce Type: new
Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platform resources. Current n…