A routing framework maintains three parallel 3D feature streams for LiDAR, 4D radar, and fusion, with a lightweight router using weather prompts to dynamically weight them and auxiliary supervision to keep branches distinct, achieving SOTA on K-Radar.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
FusionSense uses server-side fusion learning, filter-out-safe labels, and edge compaction to enable runtime-adaptive multimodal sensing that cuts energy up to 33x while preserving task quality on RGB+Depth data.
AutoAWG generates controllable adverse weather automotive videos via semantics-guided adaptive multi-control fusion and vanishing-point-anchored temporal synthesis from static images, reducing FID by 50% and FVD by 16.1% on nuScenes without first-frame conditioning.
citing papers explorer
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Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection
A routing framework maintains three parallel 3D feature streams for LiDAR, 4D radar, and fusion, with a lightweight router using weather prompts to dynamically weight them and auxiliary supervision to keep branches distinct, achieving SOTA on K-Radar.
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FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
FusionSense uses server-side fusion learning, filter-out-safe labels, and edge compaction to enable runtime-adaptive multimodal sensing that cuts energy up to 33x while preserving task quality on RGB+Depth data.
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AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
AutoAWG generates controllable adverse weather automotive videos via semantics-guided adaptive multi-control fusion and vanishing-point-anchored temporal synthesis from static images, reducing FID by 50% and FVD by 16.1% on nuScenes without first-frame conditioning.