Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection

TL;DR

Uni3D introduces a unified 3D object detection framework that effectively combines multiple datasets, addressing data and taxonomy differences to improve generalization and detection accuracy across diverse datasets.

Contribution

This paper proposes Uni3D, a simple yet effective method with data-level correction and semantic coupling modules for multi-dataset 3D detection, enhancing generalization.

Findings

Outperforms single-dataset detectors on multiple dataset consolidations

Achieves 1.04x parameter efficiency over baseline detectors

Demonstrates effective handling of data and taxonomy differences

Abstract

Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deployed in another dataset. In this paper, we study the task of training a unified 3D detector from multiple datasets. We observe that this appears to be a challenging task, which is mainly due to that these datasets present substantial data-level differences and taxonomy-level variations caused by different LiDAR types and data acquisition standards. Inspired by such observation, we present a Uni3D which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data-level and taxonomy-level differences, respectively. Our method is simple and easily combined with many 3D object detection baselines such as PV-RCNN and Voxel-RCNN, enabling them to effectively learn from multiple off-the-shelf 3D datasets to obtain more discriminative and generalizable representations. Experiments are conducted on many dataset consolidation settings including Waymo-nuScenes, nuScenes-KITTI, Waymo-KITTI, and Waymo-nuScenes-KITTI consolidations. Their results demonstrate that Uni3D exceeds a series of individual detectors trained on a single dataset, with a 1.04x parameter increase over a selected baseline detector. We expect this work will inspire the research of 3D generalization since it will push the limits of perceptual performance.