PIXOR：实时3D物体检测点云解析

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"Yang_PIXOR_Real-Time_3D_CVPR_2018_paper.pdf" 在自动驾驶领域，实时三维（3D）物体检测是至关重要的技术，它对行车安全起着决定性作用。然而，现有的3D物体检测方法由于处理高维度的点云数据，计算成本较高，无法满足实时性的需求。这篇名为"PIXOR: Real-time 3D Object Detection from Point Clouds"的论文，由Bin Yang、Wenjie Luo和Raquel Urtasun等人在2018年的CVPR会议上发表，他们当时都隶属于Uber Advanced Technologies Group和多伦多大学。论文提出了一个创新的解决方案，即PIXOR，这是一种无需提案（proposal-free）、单阶段的检测器，能够从像素级的神经网络预测中解码出有向的3D物体估计。PIXOR的关键在于，它巧妙地将场景转换到鸟瞰视角（Bird's Eye View, BEV），从而更高效地利用3D数据。通过这种表示方式，不仅能减少数据的复杂性，还能保持检测的准确性。网络架构的设计和模型优化都充分考虑了在保持高精度的同时提高实时效率。输入表示方式、网络结构以及模型优化策略均被精心设计，以平衡这两个关键因素。为了验证PIXOR的有效性，研究者在两个数据集上进行了测试：一个是Kitti BEV物体检测基准，另一个是大规模的3D车辆检测基准。实验结果显示，PIXOR在平均精度（Average Precision, AP）上显著超越了其他最先进的方法。这表明，PIXOR在保持高性能的同时，成功实现了实时3D物体检测，对于自动驾驶系统中的实时决策有着重要的实际应用价值。通过这项工作，研究者为解决自动驾驶中的关键问题提供了一个高效且准确的解决方案，对于未来智能交通系统的发展具有重要意义。

PIXOR: Real-time 3D Object Detection from Point Clouds

Bin Yang, Wenjie Luo, Raquel Urtasun

Uber Advanced Technologies Group

University of Toronto

{byang10, wenjie, urtasun}@uber.com

Abstract

We address the problem of real-time 3D object detec-

tion from point clouds in the context of autonomous driv-

ing. Speed is critical as detection is a necessary compo-

nent for safety. Existing approaches are, however, expensive

in computation due to high dimensionality of point clouds.

We utilize the 3D data more efﬁciently by representing the

scene from the Bird’s Eye View (BEV), and propose PIXOR,

a proposal-free, single-stage detector that outputs oriented

3D object estimates decoded from pixel-wise neural net-

work predictions. The input representation, network archi-

tecture, and model optimization are specially designed to

balance high accuracy and real-time efﬁciency. We validate

PIXOR on two datasets: the KITTI BEV object detection

benchmark, and a large-scale 3D vehicle detection bench-

mark. In both datasets we show that the proposed detector

surpasses other state-of-the-art methods notably in terms of

Average Precision (AP), while still runs at 10 FPS.

1. Introduction

Over the last few years we have seen a plethora of meth-

ods that exploit Convolutional Neural Networks to produce

accurate 2D object detections, typically from a single image

[12, 11, 28, 4, 27, 23]. However, in robotics applications

such as autonomous driving we are interested in detecting

objects in 3D space. This is fundamental for motion plan-

ning in order to plan a safe route.

Recent approaches to 3D object detection exploit differ-

ent data sources. Camera based approaches utilize either

monocular [

1] or stereo images [2]. However, accurate 3D

estimation from 2D images is difﬁcult, particularly in long

ranges. With the popularity of inexpensive RGB-D sen-

sors such as Microsoft Kinect, Intel RealSense and Apple

PrimeSense, several approaches that utilize depth informa-

tion and fuse them with RGB images have been developed

[

32, 33]. They have been shown to achieve signiﬁcant per-

formance gains over monocular methods. In the context

of autonomous driving, high-end sensor like LIDAR (Light

Detection And Ranging) is more common because higher

accuracy is needed for safety. The major difﬁculty in deal-

ing with LIDAR data is that the sensor produces unstruc-

tured data in the form of a point cloud containing typically

around 10

3D points per 360-degree sweep. This poses a

large computational challenge for modern detectors.

Different forms of point cloud representation have been

explored in the context of 3D object detection. The main

idea is to form a structured representation where standard

convolution operation can be applied. Existing representa-

tions are mainly divided into two types: 3D voxel grids and

2D projections. A 3D voxel grid transforms the point cloud

into a regularly spaced 3D grid, where each voxel cell can

contain a scalar value (e.g., occupancy) or vector data (e.g.,

hand-crafted statistics computed from the points within that

voxel cell). 3D convolution is typically applied to extract

high-order representation from the voxel grid [

6]. However,

since point clouds are sparse by nature, the voxel grid is

very sparse and therefore a large proportion of computation

is redundant and unnecessary. As a result, typical systems

[

6, 37, 20] only run at 1-2 FPS.

An alternative is to project the point cloud onto a plane,

which is then discretized into a 2D image based representa-

tion where 2D convolutions are applied. During discretiza-

tion, hand-crafted features (or statistics) are computed as

pixel values of the 2D image [

3]. Commonly used projec-

tions are range view (i.e., 360-degree panoramic view) and

bird’s eye view (i.e., top-down view). These 2D projection

based representations are more compact, but they bring in-

formation loss during projection and discretization. For ex-

ample, range-view projection will have distorted object size

and shape. To alleviate the information loss, MV3D [

3] pro-

poses to fuse the 2D projections with the camera image to

bring additional information. However, the fused model has

nearly linear computation cost with respect to the number of

input modals, making real-time application infeasible.

In this paper, we propose an accurate real-time 3D object

detector, which we call PIXOR (ORiented 3D object de-

tection from PIXel-wise neural network predictions), that

operates on 3D point clouds. PIXOR is a single-stage,

7652

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