The KITTI Vision Benchmark Suite

Method

Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency [WRP]
NIL

Submitted on 17 Aug. 2021 00:19 by
Aditya Ganeshan (Brown)

Running time:		1 s
Environment:		GPU @ >3.5 Ghz (Python)

Method Description:

we propose a novel label propagation method, termed
Warp-Refine Propagation, that combines semantic
cues with geometric cues to efficiently auto-label
videos. Our method learns to refine geometrically-
warped labels and infuse them with learned semantic
priors in a semi-supervised setting by leveraging
cycle-consistency across time. This work has been
accepted in ICCV 2021.

Parameters:

None

Latex Bibtex:

@InProceedings{Ganeshan_2021_ICCV,
author = {Ganeshan, Aditya and Vallet, Alexis and Kudo, Yasunori and Maeda, Shin-
ichi and Kerola, Tommi and Ambrus, Rares and Park, Dennis and Gaidon, Adrien},
title = {Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-
consistency},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV)},
month = {October},
year = {2021}
}

Detailed Results

This page provides detailed results for the method(s) selected. For the first 20 test images, we display the original image, the color-coded result and an error image. The error image contains 4 colors:
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

IoU class	iIoU class	IoU category	iIoU category
76.44	50.92	89.63	73.69

Error

The KITTI Vision Benchmark Suite

A project of Karlsruhe Institute of Technologyand Toyota Technological Institute at Chicago

Method

Detailed Results

Test Set Average

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A project of Karlsruhe Institute of Technology
and Toyota Technological Institute at Chicago