Table 1 Time and storage complexity of the proposed feature-vector-based cost aggregation algorithm

Feature vector computation

O _a (2×W×H)

Feature vector comparison

O _b (W×H×M) for C and O _b (W×H×(2w+1)) for P

Construction and update of Γ

\(O_{c}\left ((2w+1)^{2} \times W \times M\right)\) and O _c (3×W×H×M), respectively

Construction and update of Φ

\(O_{c} \left (\frac {(2w+1)^{2}}{2} \times W\right)\) and O _c (3/2×W×H×(2w+1)), respectively

Cost aggregation for all pixels

O _c (3×(2w+1)×W×H×M)

Storage complexity

Unit: Number of floating-point numbers

The cost volume array C

Maximum: W×H×M

Minimum: (2w+1)×W×M

Array P

Maximum: (2w+1)×W×M

Minimum: (2w+1)²×W

Matrices F _l and F _r

L×W entries, depending on the type of feature vector

Matrices Γ and Φ

W ²

Vector a

M

Vector w

2w+1