SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Feature Extractor: In Fig. G, we show a visualization of the architecture of the feature extractor. The same network architecture is used for extracting context features and for extracting matching features, except the former uses no normalization and output dimension  $D = 384$  while the latter uses instance normalization and  $D = 128$ . We use instance normalization for the matching features since they should be calculated independently for each input image in a batch, which other flow-based networks (36; 38; 37) do as well. Our feature extractor is similar to the architecture used in DROID-SLAM (37), but half the size. Consequently, the output resolution is 1/4 of the image resolution, as opposed to 1/8th. Since DPVO only tracks a sparse collection of patches instead of predicting dense flow, we can afford to use higher spatial-resolution features without significant memory overhead.
Figure F: Architecture of the Update Operator, excluding non-learnable layers such as the correlation layer and the bundle-adjustment layer.
2D-Conv 7x7 (32) Stride 2
Residual-Block (32)  
Stride 1
Residual-Block (64)  
Stride 2
2D-Conv 1x1 (D)
Figure G: Architecture of the feature extractors.  $D = 128$  for the matching-feature extractor and  $D = 384$  for the context-feature extractor.
H Predicted Trajectories on EuRoC
Figure H: Trajectory predictions on the EuRoC test dataset.
I Predicted Trajectories on TartanAir
Figure I: Trajectory predictions on the TartanAir test dataset.