Spatial Embedding-based 3D Proposal [la] [SERCNN]

Submitted on 31 Oct. 2019 15:59 by
Jin Fang (Baidu)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Currently, in Autonomous Driving (AD), most of
the 3D object detection frameworks (either
anchor- or anchor-freebased) consider the
detection as a Bounding Box (BBox) regression
problem. However, this compact representation is
not sufficient to explore all the information of
the objects. To tackle this problem, we propose a
simple but practical detection framework to
jointly predict the 3D BBox and instance
segmentation. For instance segmentation, we
propose a Spatial Embeddings (SEs) strategy to
assemble all foreground points into their
corresponding object centers. Base on the SE
results, the object proposals can be generated
based on a simple clustering strategy. For each
cluster, only one proposal is generated.
Therefore, the NonMaximum Suppression (NMS)
process is no longer needed here. Finally, with
our proposed instance-aware ROI pooling, the BBox
is refined by a second-stage network.
Latex Bibtex:
title={Joint 3D Instance Segmentation and
Object Detection for Autonomous Driving},
author={Zhou, Dingfu and Fang, Jin and Song,
Xibin and Liu, Liu and Yin, Junbo and Dai, Yuchao
and Li, Hongdong and Yang, Ruigang},
booktitle={Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).

Benchmark Easy Moderate Hard
Car (Detection) 96.33 % 94.42 % 89.96 %
Car (Orientation) 96.31 % 94.24 % 89.71 %
Car (3D Detection) 87.74 % 78.96 % 74.30 %
Car (Bird's Eye View) 94.11 % 88.10 % 83.43 %
This table as LaTeX

2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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