Object detection is widely used in real-world applications such as autonomous driving, yet adversarial camouflage poses a significant threat by deceiving detectors from multiple viewpoints. Existing techniques struggle to maintain consistent attack efficacy across different viewpoints. To address this, we propose GRAC, an adversarial camouflage framework that enhances attack effectiveness across viewpoints and distances. First, we identify conflicts in gradient updates across angles and introduce gradient reweighting to resolve them, enabling coordinated optimization. Second, we model light interactions to simulate illumination changes, improving robustness under varying lighting conditions. Additionally, we address non-uniform texture updates arisen from inconsistent sampling density during rendering by applying pooling-based texture regularization to improve smoothness. Extensive experiments in both simulated and physical environments demonstrate that GRAC outperforms existing methods across diverse conditions.