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FastPillars: A Deployment-friendly Pillar-based 3D Detector (2302.02367v6)

Published 5 Feb 2023 in cs.CV and cs.RO

Abstract: The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios. Existing BEV-based (i.e., Bird Eye View) detectors favor sparse convolutions (known as SPConv) to speed up training and inference, which puts a hard barrier for deployment, especially for on-device applications. In this paper, to tackle the challenge of efficient 3D object detection from an industry perspective, we devise a deployment-friendly pillar-based 3D detector, termed FastPillars. First, we introduce a novel lightweight Max-and-Attention Pillar Encoding (MAPE) module specially for enhancing small 3D objects. Second, we propose a simple yet effective principle for designing a backbone in pillar-based 3D detection. We construct FastPillars based on these designs, achieving high performance and low latency without SPConv. Extensive experiments on two large-scale datasets demonstrate the effectiveness and efficiency of FastPillars for on-device 3D detection regarding both performance and speed. Specifically, FastPillars delivers state-of-the-art accuracy on Waymo Open Dataset with 1.8X speed up and 3.8 mAPH/L2 improvement over CenterPoint (SPConv-based). Our code is publicly available at: https://github.com/StiphyJay/FastPillars.

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Citations (22)

Summary

  • The paper introduces a novel MAPE module that boosts small object detection, achieving a +1.6 mAPH L2 gain for pedestrian detection on the Waymo dataset.
  • It reallocates computation in the backbone to earlier stages, enhancing geometric feature extraction while reducing latency by 14% compared to previous methods.
  • The approach eliminates the need for SPConv, ensuring compatibility with TensorRT and enabling efficient real-time deployment on edge devices.

An Analysis of "FastPillars: A Deployment-friendly Pillar-based 3D Detector"

In the paper "FastPillars: A Deployment-friendly Pillar-based 3D Detector," the authors present an innovative approach to 3D object detection that addresses both accuracy and deployment efficiency issues in real-world autonomous driving applications. The paper introduces FastPillars, a pillar-based 3D detection model specifically designed to be compatible with on-device applications without relying on Sparse Convolutions (SPConv).

The paper starts by identifying a significant gap in the current landscape of 3D object detection: the reliance on SPConv for processing LiDAR data, which poses challenges in deployment and on-device performance. To overcome this challenge, the authors propose a novel architecture consisting entirely of standard convolutions, thereby achieving compatibility with platforms such as TensorRT and supporting network quantization.

Key Innovations and Results:

  1. Max-and-Attention Pillar Encoding (MAPE) Module: A significant contribution of the paper is the MAPE module, which addresses the limitations of existing max-pooling techniques used in pillar encoding. By incorporating attention mechanisms, MAPE selectively highlights important local features while integrating them into a more comprehensive pillar representation. This innovation is particularly beneficial for small object detection, yielding a notable improvement of +1.6 mAPH L2 for pedestrian detection on the Waymo dataset.
  2. Backbone Design with Computation Reallocation: The paper provides a fresh perspective on backbone design by reallocating computational resources to earlier stages, exploiting the inherent modality differences between LiDAR point clouds and 2D images. This adjusted distribution of resources enhances geometric feature extraction from raw points, yielding superior accuracy without increasing computational overhead.
  3. Lightweight Re-parameterized Structures: Inspired by the YOLO series' success in 2D object detection, FastPillars adopts re-parameterization strategies in its backbone to reduce computation costs while preserving performance. This change leads to a 14% reduction in latency and a 0.6 mAPH L2 gain, reinforcing the importance of structural re-parameterization in achieving efficient inference.

The experimental results corroborate these innovations. FastPillars achieves state-of-the-art performance on large-scale datasets such as nuScenes and Waymo. It surpasses existing methods, delivering a 1.8× speed increase and a 3.8 mAPH/L2 improvement over CenterPoint—a well-established SPConv-based method. Its compatibility with TensorRT further allows FastPillars to run in real-time on edge devices, including constrained hardware environments relevant to automotive applications.

Implications and Future Directions:

The deployment-friendly nature of FastPillars positions it as an influential contribution to the field of autonomous driving and robotics. By eliminating SPConv, it lowers the barrier to deploying high-performance LiDAR perception systems, paving the way for broader adoption in embedded environments.

Looking forward, this work points to several intriguing research directions:

  • Architecture Enhancements: Further exploration of neural architecture search (NAS) techniques could refine the computation allocation strategies proposed, considering factors like resolution and stage depths.
  • Cross-Domain Learning: Application of similar innovative encoding and design techniques to other domains where point-based spatial data is crucial, such as satellite imagery or medical image analysis.
  • Edge Deployment: Exploration and optimization of FastPillars in various edge and IoT environments could assist in understanding its adaptability and performance in diverse real-world scenarios.

In conclusion, "FastPillars: A Deployment-friendly Pillar-based 3D Detector" offers a compelling approach to balancing accuracy, efficiency, and deployability in 3D object detection for autonomous systems. The paper’s insights and methodologies are expected to significantly impact both academic research and industrial applications in LiDAR-based perception.

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