SHIFT3D: 3D検出器を欺く困難な入力の合成
SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors
署名距離関数で物体形状を表現し、勾配信号を用いて3D物体検出器を混乱させる構造的に妥当な3D形状を生成する微分可能パイプラインを提案。
著者: Hongge Chen, Zhao Chen, Gregory P. Meyer, Dennis Park, Carl Vondrick, Ashish Shrivastava, Yuning Chai
分類: cs.CV, cs.CR, cs.LG, cs.RO
原文アブストラクト
We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors. In safety-critical applications like autonomous driving, discovering such novel challenging objects can offer insight into unknown vulnerabilities of 3D detectors. By representing objects with a signed distanced function (SDF), we show that gradient error signals allow us to smoothly deform the shape or pose of a 3D object in order to confuse a downstream 3D detector. Importantly, the objects generated by SHIFT3D physically differ from the baseline object yet retain a semantically recognizable shape. Our approach provides interpretable failure modes for modern 3D object detectors, and can aid in preemptive discovery of potential safety risks within 3D perception systems before these risks become critical failures.
