Redundant Queries in DETR-Based 3D Detection Methods: Unnecessary and Prunable

ICLR 2025 Conference Submission1175 Authors

Keywords: DETR, 3D object detection, Query pruning

Abstract: Query-based models are extensively used in 3D object detection tasks, with a wide range of pre-trained checkpoints readily available online. However, despite their popularity, these models often require an excessive number of object queries, far surpassing the actual number of objects to detect. The redundant queries result in unnecessary computational and memory costs. In this paper, we find that not all queries contribute equally --- a significant portion of queries have a much smaller impact compared to others. Based on this observation, we propose an embarrassingly simple approach that Gradually Prunes Queries (GPQ) according to classification scores that queries generated. Compared to existing pruning methods, our method introduces no additional learnable parameters. GPQ is easy to implement to any query-based method by integrating it in after-training fine-tune using an existing checkpoint. By using our method, one can easily generate several different models with fewer queries using an checkpoint has exicessive queries. Experiments on various advanced 3D detectors show that GPQ effectively reduces redundant queries while maintaining performance. Using our method, model inference on desktop GPUs can be accelerated by up to 1.31x. Moreover, after deployment on edge devices, it achieves up to a 67.86\% reduction in FLOPs and a 76.38\% decrease in inference time. The code will be available soon.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 1175