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.

SHIFT3D 通过使用有符号距离函数（SDF）表示对象，并利用梯度错误信号来平滑地改变 3D 对象的形状或姿态，从而生成既具有结构合理性又对 3D 物体探测器具有挑战性的 3D 形状。这种方法为现代 3D 物体探测器提供了可解释的失败模式，并可以在潜在的安全风险发展为关键故障之前帮助及时发现潜在的安全风险。