The KITTI Vision Benchmark Suite

Method

GraphAVO [GraphAVO]
[Anonymous Submission]

Submitted on 28 Mar. 2024 15:09 by
[Anonymous Submission]

Running time:		0.01 s
Environment:		GPU @ 1.5 Ghz (Python)

Method Description:

GraphAVO concentrates simultaneously on the adjacent
and interval co-visibility correspondence to
establish a feature and pose graph optimization for
pose consistency. Inspired by the insight of pose
graph optimization, deep visual correspondences are
refined. Furthermore, considering various degrees of
pixel movement, GraphAVO perceives subtle and
distinct motion cues gathered from receptive fields
of varying extents.

Parameters:

0.1

Latex Bibtex:

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.