← 研踪 PaperTrailarXiv:2610.35110 · v1
Understanding and Repairing Cooperative Perception under Adverse Weather
首次提交 2026-10-26 17:49
版本更新 2026-10-26 17:49
雷达观测 2026-10-29 00:36
摘要
A cooperative 3D detector can miss a vehicle even when the LiDAR points on it remain unchanged. We study this failure under adverse weather as Weather-Induced Context Interference. To our knowledge, this is the first controlled study of how weather-related exterior changes remove cooperative benefits while preserving all points inside annotated objects. Controlled experiments on simulated and real roadside inputs show that exterior loss changes object representations before fusion and removes previously useful assistance. On real inputs, restoring object-neighborhood features recovers 30 of 31 induced failures; three equal-area exterior controls recover none. The failure condition also appears with a second fusion model. These findings motivate Adverse-Weather Cooperative Repair (AWCR), with separate implementations for the available supervision. Point Classifier filters added weather returns in simulation. On real inputs, Local Correction refines candidates and Quality Selection checks them against current measurements, without naturally paired clear/adverse-weather recordings. Candidate tracing shows that correction supplies accurate geometry for some targets that original candidates cannot localize at the evaluation threshold. On natural adverse-weather CoInfra development data, AWCR improves car bird's-eye-view (BEV) AP@0.7 by 4.66 points with V2X-ViT and 5.56 with AttFuse. On simulated V2X-R fog, it improves vehicle BEV AP@0.7 by 4.47 points at a fixed 4,096-candidate budget.
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v1 · 2026-10-26 17:49 · 最新观测版本
Understanding and Repairing Cooperative Perception under Adverse Weather
A cooperative 3D detector can miss a vehicle even when the LiDAR points on it remain unchanged. We study this failure under adverse weather as Weather-Induced Context Interference. To our knowledge, this is the first controlled study of how weather-related exterior changes remove cooperative benefits while preserving all points inside annotated objects. Controlled experiments on simulated and real roadside inputs show that exterior loss changes object representations before fusion and removes previously useful assistance. On real inputs, restoring object-neighborhood features recovers 30 of 31 induced failures; three equal-area exterior controls recover none. The failure condition also appears with a second fusion model. These findings motivate Adverse-Weather Cooperative Repair (AWCR), with separate implementations for the available supervision. Point Classifier filters added weather returns in simulation. On real inputs, Local Correction refines candidates and Quality Selection checks them against current measurements, without naturally paired clear/adverse-weather recordings. Candidate tracing shows that correction supplies accurate geometry for some targets that original candidates cannot localize at the evaluation threshold. On natural adverse-weather CoInfra development data, AWCR improves car bird's-eye-view (BEV) AP@0.7 by 4.66 points with V2X-ViT and 5.56 with AttFuse. On simulated V2X-R fog, it improves vehicle BEV AP@0.7 by 4.47 points at a fixed 4,096-candidate budget.
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