The KITTI Vision Benchmark Suite

Method

DiPFormer [DiPFormer]

Submitted on 9 Sep. 2024 03:21 by
Siyu Chen (JiMei University)

Running time:		0.01 s
Environment:		1 GPU @ 2.5 Ghz (C/C++)

Method Description:

SCI Lab

Parameters:

SCI Lab

Latex Bibtex:

@misc{chen2024depthmattersexploringdeep,
title={Depth Matters: Exploring Deep
Interactions of RGB-D for Semantic Segmentation in
Traffic Scenes},
author={Siyu Chen and Ting Han and Changshe
Zhang and Weiquan Liu and Jinhe Su and Zongyue
Wang and Guorong Cai},
year={2024},
eprint={2409.07995},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2409.07995},
}

Evaluation in Bird's Eye View

Benchmark	MaxF	AP	PRE	REC	FPR	FNR
UM_ROAD	97.29 %	92.66 %	97.37 %	97.20 %	1.20 %	2.80 %
UMM_ROAD	98.19 %	94.80 %	97.88 %	98.50 %	2.34 %	1.50 %
UU_ROAD	96.83 %	91.21 %	96.57 %	97.09 %	1.12 %	2.91 %
URBAN_ROAD	97.57 %	92.94 %	97.34 %	97.79 %	1.47 %	2.21 %

This table as LaTeX

Behavior Evaluation

Benchmark	PRE-20	F1-20	HR-20	PRE-30	F1-30	HR-30	PRE-40	F1-40	HR-40

This table as LaTeX

Road/Lane Detection

The following plots show precision/recall curves for the bird's eye view evaluation.

This figure as: png eps pdf

Distance-dependent Behavior Evaluation

The following plots show the F1 score/Precision/Hitrate with respect to the longitudinal distance which has been used for evaluation.

Visualization of Results

The following images illustrate the performance of the method qualitatively on a couple of test images. We first show results in the perspective image, followed by evaluation in bird's eye view. Here, red denotes false negatives, blue areas correspond to false positives and green represents true positives.

This figure as: png