KITTI-360

Method

Fully Convolutional Network [FCN]

Submitted on 21 Oct. 2021 13:45 by
Yiyi Liao (MPI)

Running time:		0.2 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

CVPR 2015. Trained and evaluated by KITTI-360 authors.

Parameters:

none

Latex Bibtex:

@InProceedings{Long2015CVPR,
author = {Long, Jonathan and Shelhamer,
Evan and Darrell, Trevor},
title = {Fully Convolutional Networks for
Semantic Segmentation},
booktitle = {CVPR},
year = {2015},
}

Detailed Results

This page provides detailed results for the method(s) selected. For the first 10 test images, we display the original image, the color-coded result and an error image. The error image contains 4 colors weighted by the confidence of the pseudo-ground truth:
red: the pixel has the wrong label and the wrong category
yellow: the pixel has the wrong label but the correct category
green: the pixel has the correct label
black: the groundtruth label is not used for evaluation

Test Set Average

road	sidewalk	building	wall	fence	pole	traffic light	traffic sign	vegetation	terrain	sky	person	rider	car	truck	motorcycle	bicycle	mIoU class
95.62	84.52	84.07	43.35	38.55	31.10	0.00	38.04	90.62	85.69	91.22	40.54	29.33	94.56	42.39	28.42	0.00	54.00

flat	construction	object	nature	human	vehicle	sky	mIoU category
96.19	82.61	35.78	91.51	51.89	94.26	91.22	77.64

Error