On the Feasibility and Opportunity of Autoregressive 3D Object Detection
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 decodi...
| Authors | Zanming Huang, Jinsu Yoo, Sooyoung Jeon et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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