Dimensions overall score 3.0

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• R-PGA utilizes Relightable 3D Gaussian Splatting (3DGS) for photo-realistic reconstruction and physically disentangled attributes to decouple intrinsic material from lighting.method 95%
• R-PGA employs a hybrid rendering pipeline that uses Relightable 3DGS for foreground rendering and a pre-trained image translation model for synthesizing plausible relighted backgrounds.method 95%
• The Hard Physical Configuration Mining (HPCM) module is designed to actively mine worst-case physical configurations and suppress their corresponding loss peaks, flattening the loss landscape.method 95%
• Current physical adversarial camouflage methods are brittle in complex dynamic scenarios and fail to generalize across diverse geometric and radiometric variations.limitation 90%
• Reliance on coarse, oversimplified simulations induces a significant domain gap, confining optimization to a biased feature space.limitation 90%
• Standard strategies targeting average performance result in a rugged loss landscape, leaving camouflage vulnerable to configuration shifts.limitation 90%
• R-PGA effectively flattens the adversarial loss landscape within the multi-dimensional physical parameter space.result 90%
• The paper conducts comprehensive experiments in both digital and physical domains, comparing R-PGA against a broader range of SOTA methods across four distinct physical configuration dimensions.method 90%

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