KITTI-360

Method

NeRF: Neural Radiance Fields + PSPNet [NeRF + PSPNet]

Submitted on 8 Apr. 2022 16:01 by
Yiyi Liao (MPI)

Running time:		15 s
Environment:		GPU @ 2.5 Ghz (Python)

Method Description:

A two-stage baseline implemented by KITTI-360 authors. Appearance
synthesis is performed by NeRF. Semantic segmentation predictions are
then obtained by applying a pre-trained PSPNet to the synthesized
images.

Parameters:

None

Latex Bibtex:

@inproceedings{mildenhall2020nerf,
title={NeRF: Representing Scenes as Neural Radiance Fields for View
Synthesis},
author={Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik
and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng},
year={2020},
booktitle={ECCV},
},
@InProceedings{Zhao2017CVPR,
author = {Hengshuang Zhao and Jianping Shi
and Xiaojuan Qi and Xiaogang Wang and Jiaya Jia},
title = {Pyramid Scene Parsing Network},
booktitle = {CVPR},
year = {2017},
}

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

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road	sidewalk	building	wall	fence	pole	traffic light	traffic sign	vegetation	terrain	sky	rider	car	truck	motorcycle	mIoU class
88.95	71.05	55.37	31.88	13.17	9.82	0.00	6.83	80.72	66.69	84.22	0.00	85.07	0.00	1.07	49.57

flat	construction	object	nature	human	vehicle	sky	mIoU category
90.00	59.52	10.21	85.29	0.00	85.60	84.22	69.14

Error