Question
Dear authors,
Thank you very much for your impressive work and the open-source implementation of StreamVLN. I’ve learned a lot from both the paper and the codebase.
I would like to ask for a clarification regarding the definition of H and W in Algorithm 1 (Voxel-Based Spatial Pruning).
Based on the paper and the implementation, my understanding is that:
Starting from the raw camera resolution (e.g., 640×480), the image is first resized/cropped to the vision tower’s input resolution (e.g., 384×384 for SigLIP).
It is then divided into patches (with patch size 14×14), resulting in num_patches_per_side = floor(384 / 14) = 27.
Therefore, H and W correspond to num_patches_per_side (27×27 in this example), and the total number of visual tokens per frame is H × W = 729.
Could you please confirm whether my understanding is correct?
Thank you again for your time and help!
Question
Dear authors,
Thank you very much for your impressive work and the open-source implementation of StreamVLN. I’ve learned a lot from both the paper and the codebase.
I would like to ask for a clarification regarding the definition of H and W in Algorithm 1 (Voxel-Based Spatial Pruning).
Based on the paper and the implementation, my understanding is that:
Starting from the raw camera resolution (e.g., 640×480), the image is first resized/cropped to the vision tower’s input resolution (e.g., 384×384 for SigLIP).
It is then divided into patches (with patch size 14×14), resulting in num_patches_per_side = floor(384 / 14) = 27.
Therefore, H and W correspond to num_patches_per_side (27×27 in this example), and the total number of visual tokens per frame is H × W = 729.
Could you please confirm whether my understanding is correct?
Thank you again for your time and help!