用序列生成方式做3D目标检测，无需锚框和去重。
On the Feasibility and Opportunity of Autoregressive 3D Object Detection
- 将检测任务转为近到远的序列生成，每物体用离散序列表示。
- 在nuScenes上性能媲美主流方法，且无需锚框或NMS。
- 支持语言模型技术迁移，适合想尝试新范式的研究者。
基于激光雷达的3D目标检测器通常依赖带手工设计组件（如锚框分配和非极大值抑制）的候选框头，导致训练复杂且扩展性差。我们提出AutoReg3D，一种自回归3D检测器，将检测任务建模为序列生成。给定点云特征后，AutoReg3D按近到远顺序生成物体，并将每个物体编码为包含中心、尺寸、方向、速度和类别的短离散序列。这种近到远排序符合激光雷达几何特性——近处物体遮挡远处但反之不成立，从而在训练中实现直接教师强制，在测试时支持自回归解码。AutoReg3D兼容多种点云输入或主干网络，在nuScenes上达到与现有方法相当的性能，且无需锚框或NMS。超越性能持平，序列化形式还使得可引入语言模型先进方法，如针对任务对齐目标的GRPO式强化学习。这些结果表明自回归解码是激光雷达检测的一种可行且灵活的替代方案，也为将现代序列建模工具引入3D感知开辟了路径。
原文摘要 · Abstract (English)
LiDAR-based 3D object detectors typically rely on proposal heads with hand-crafted components like anchor assignment and non-maximum suppression (NMS), complicating training and limiting extensibility. We present AutoReg3D, an autoregressive 3D detector that casts detection as sequence generation. Given point-cloud features, AutoReg3D emits objects in a range-causal (near-to-far) order and encodes each object as a short, discrete-token sequence consisting of its center, size, orientation, velocity, and class. This near-to-far ordering mirrors LiDAR geometry--near objects occlude far ones but not vice versa--enabling straightforward teacher forcing during training and autoregressive decoding at test time. AutoReg3D is compatible across diverse point-cloud or backbones and attains competitive nuScenes performance without anchors or NMS. Beyond parity, the sequential formulation unlocks language-model advances for 3D perception, including GRPO-style reinforcement learning for task-aligned objectives. These results position autoregressive decoding as a viable, flexible alternative for LiDAR-based detection and open a path to importing modern sequence-modeling tools into 3D perception.
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