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.

翻译：我们提出SHIFT3D，这一可微流水线能够生成结构上合理但使3D物体检测器难以应对的3D形状。在自动驾驶等安全关键应用中，发现此类新颖的挑战性物体有助于揭示3D检测器未知的脆弱性。通过使用符号距离函数（SDF）表示物体，我们证明梯度误差信号允许我们平滑地变形3D物体的形状或姿态，从而迷惑下游3D检测器。重要的是，SHIFT3D生成的物体在物理上不同于基准物体，但仍保留语义可识别的形状。我们的方法为现代3D物体检测器提供了可解释的失效模式，并有助于在3D感知系统的潜在安全风险升级为关键故障前主动发现这些风险。