arXiv Computer Vision
VkVIO: Cross-platform GPU Acceleration for Visual-Inertial Odometry with Vulkan
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LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning
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Lightweight and Resource-Efficient Perception for Robotic Guide Dogs
The paper examines how multi‑camera streaming perception systems perform on heterogeneous edge platforms that share resources with other workloads. Using two end‑to‑end pipelines on a single GPU–NPU platform, the authors show that isolated single‑stream evaluations can mislead deployment decisions: while the GPU pipeline appears superior in isolation, GPU‑local contention causes deadline misses that make detections stale and can reverse the preferred placement. The study finds that the NPU pipeline, though less accurate for small and medium objects, nearly matches the GPU on large objects, and that under high contention the best placement shifts from All‑GPU to All‑NPU, achieving a 5.2× improvement in worst‑stream sAP. The authors argue that evaluation metrics should include contention sweeps, deadline‑miss rates, and worst‑stream sAP in addition to mean sAP to capture severe single‑stream degradation.
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Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures
arXiv:2608. 14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure.
arXiv Computer Vision
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EVIS: Real-Time Event Camera Simulation with Multimodal Supervision in NVIDIA Isaac Sim
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LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning
Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through visual perception. A representative example is Human...