  Collaborative perception is dedicated to tackling the constraints of
single-agent perception, such as occlusions, based on the multiple agents'
multi-view sensor inputs. However, most existing works assume an ideal
condition that all agents' multi-view cameras are continuously available. In
reality, cameras may be highly noisy, obscured or even failed during the
collaboration. In this work, we introduce a new robust camera-insensitivity
problem: how to overcome the issues caused by the failed camera perspectives,
while stabilizing high collaborative performance with low calibration cost? To
address above problems, we propose RCDN, a Robust Camera-insensitivity
collaborative perception with a novel Dynamic feature-based 3D Neural modeling
mechanism. The key intuition of RCDN is to construct collaborative neural
rendering field representations to recover failed perceptual messages sent by
multiple agents. To better model collaborative neural rendering field, RCDN
first establishes a geometry BEV feature based time-invariant static field with
other agents via fast hash grid modeling. Based on the static background field,
the proposed time-varying dynamic field can model corresponding motion vectors
for foregrounds with appropriate positions. To validate RCDN, we create
OPV2V-N, a new large-scale dataset with manual labelling under different camera
failed scenarios. Extensive experiments conducted on OPV2V-N show that RCDN can
be ported to other baselines and improve their robustness in extreme
camera-insensitivity settings. Our code and datasets will be available soon.
