On the Feasibility and Opportunity of Autoregressive 3D Object Detection

Zanming Huang; Jinsu Yoo; Sooyoung Jeon; Zhenzhen Liu; Mark Campbell; Kilian Q Weinberger; Bharath Hariharan; Wei-Lun Chao; Katie Z Luo

arXiv · 2603.07985

On the Feasibility and Opportunity of Autoregressive 3D Object Detection

Zanming Huang·Jinsu Yoo·Sooyoung Jeon·Zhenzhen Liu·Mark Campbell·Kilian Q Weinberger·Bharath Hariharan·Wei-Lun Chao·Katie Z Luo

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Abstract

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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Zanming Huang, Jinsu Yoo, Sooyoung Jeon, Zhenzhen Liu, Mark Campbell, Kilian Q Weinberger, Bharath Hariharan, Wei-Lun Chao, Katie Z Luo. 2026-03-09. On the Feasibility and Opportunity of Autoregressive 3D Object Detection. https://arxiv.org/abs/2603.07985

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