KITTI-360

Method

Holistic Urban 3D Scene Understanding via Gaussian Splatting [HUGS]
[Anonymous Submission]

Submitted on 2 Dec. 2023 02:11 by
[Anonymous Submission]

Running time:		0.02 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

Our method can simultaneously synthesize novel view
appearance and semantics in a single model. To enhance the synergy
between these two tasks, we incorporated additional cues and
regularization techniques.

Parameters:

NA

Latex Bibtex:

NA

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
95.04	80.49	67.03	47.76	57.10	61.96	0.00	30.19	93.31	93.78	88.66	0.00	96.00	0.00	60.43	72.65

flat	construction	object	nature	human	vehicle	sky	mIoU category
93.85	84.18	57.71	93.68	0.00	95.77	88.66	85.64

Error