# Estimating Optical Flow with Streaming Perception and Changing Trend Aiming to Complex Scenarios
## 4 Experiments
### Quantitative Results on Standard Datasets  
We report the whole quantitative results in Table 4. To verify our design, we compare our performance with the original RAFT [7] and serval variants of that, including RAFT-A [36], GMA (and its extension) [14], CRAFT [37], and FM-RAFT [38]. Although our model has certain advantages in the trainset columns of the table, they still can hardly represent the generalizability of the models since these results are evaluated from the training sets. Here, we placed them on Table 4, just to comply with some conventional format for the reference. In addition, other columns of the table contain the estimated results of the testing sets from Sintel. The reason why we did not evaluate the KITTI 2015 testing set has been explained early in this paper, because every scene in the KITTI datasets only  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Training Data**} & \multirow{2}{*}{\(\delta\)} & \multirow{2}{*}{\(\mu\)} & \multicolumn{2}{c}{**Sintel (Train)**} & \multicolumn{2}{c}{**KITTI-15 (Train)**} \\ \cline{5-8}  & & & **Clean** & **Final** & **AEPE** & **Fl-All (\%)** \\ \hline \multirow{8}{*}{C + T} & 0.3 & 1.5 & 1.29 & **2.57** & 5.03 & 17.07 \\  & 0.3 & 1.6 & 1.25 & 2.62 & 4.99 & 17.29 \\  & 0.3 & 1.7 & 1.27 & 2.59 & 4.90 & 17.07 \\  & 0.35 & 1.5 & 1.31 & 2.60 & 4.91 & 17.24 \\  & **0.35** & **1.6** & **1.24** & 2.58 & **4.90** & **16.81** \\  & 0.35 & 1.7 & 1.24 & 2.62 & 4.99 & 16.83 \\  & 0.4 & 1.5 & 1.30 & 2.60 & 5.08 & 17.34 \\  & 0.4 & 1.6 & 1.29 & 2.58 & 4.99 & 17.51 \\  & 0.4 & 1.7 & 1.32 & 2.60 & 5.29 & 17.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid search of \(\delta\) and \(\mu\) in Equation (6) for trend cost conditional weights in the total loss function.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Data**} & \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**C-MFF**} & \multirow{2}{*}{**Our Loss**} & \multicolumn{2}{c}{**Sintel (Train)**} & \multicolumn{2}{c}{**KITTI-15 (Train)**} \\ \cline{5-8}  & & & & **Clean** & **Final** & **AEPE** & **Fl-All** \\ \hline \multirow{8}{*}{C + T} & * & & 1.43 & 2.71 & 5.04 & 17.40 \\  & * & * & 1.32 & 2.62 & 5.01 & 17.24 \\ \cline{1-1}  & * & & * & 1.39 & 2.65 & 4.95 & 16.96 \\ \cline{1-1}  & * & * & * & 1.24 & 2.58 & 4.90 & 16.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effect of the proposed pipeline combined with baseline (RAFT), C-MFF, and our specifically designed loss. Where *✓ indicates the module is introduced into the training structure.  
provides two continuous image frames, which will obviously limit the performance of our structure. According to this, we focus more on the results evaluated from the Clean version of the Sintel testing sets, and our model has improved the AEPE metric from 1.61 to 1.27 compared with original RAFT. That allows it achieve the best performance among all models listed on Table 4. For the Final version of Sintel testing sets, however, it also increased by 12.94 % (2.86 \(\rightarrow\) 2.49) compared with the original RAFT. This result is not as good as CRAFT and GMA, but it is moderately better than any other RAFT variant. We will discuss the possible explanation for this phenomenon.