Repurposing Influence Diagnostics for Low-Bit 3D Object Detection Models

3D object detection is essential for instance-level scene understanding, yet deploying computationally intensive detectors on resource-constrained devices often requires low-bit quantization. However, reducing bit width remains challenging because 3D detectors exhibit activation distributions in which high-magnitude outliers are not uniformly informative. While some outliers carry task-relevant information, others arise from incidental or nuisance patterns. Such uninformative outliers can unnecessarily broaden the activation range, leading to coarse discretization and feature distortion under low-bit quantization. To address this issue, we repurpose influence diagnostics for quantization, where influence reflects how strongly an individual activation affects task-relevance. Our approach builds on the statistical perspective that informative outliers are crucial for preserving the properties of the data distribution. This perspective aligns with the classical notion of influential observations, motivating us to identify informative outliers by assessing their impact on task-relevant information. To make this influence task-aware, we use activation gradients to capture the relevance of individual activations to the task objective. Because these gradients contain both dominant task-relevant patterns and incidental variations, we model their shared structure with a low-rank representation that retains the principal patterns to be preserved. We then assess activation influence by measuring how strongly each activation affects the resulting gradient structure. Experiments on the nuScenes benchmark demonstrate improvements across W8A8, W4A16, W4A8, and W4A4 settings, with larger gains at lower bit widths. These results suggest that activation influence provides an effective criterion for identifying informative outliers in low-bit quantization.