Dear authors,
I'd like to test your model on my custom 3D reconstructed pointclouds + rgb map.
In order to directly use the checkpoints you shared, I need to extract the normalization features from 3D mesh (as your models are all trained with the norm features).
However, I have no idea how to make the norm features from my custom dataset. I understand the normalized features are calculated in read_mesh_vertices_rgb_normal() from the face of 3D mesh. But I only have 3D pointclouds and rgb, without the mesh faces.
I initially tried to get the faces using Meshlab tool (surface reconstruction - ball pivoting), and the result is as below.
Using the 3D mesh, I followed the all preprocessing steps (xyz, rgb, norm) to extract the similar features to fit your model with the checkpoint(scanrefer_scst_vote2cap_detr_pp_XYZ_RGB_NORMAL.pth).
But the detection and captioning result was really poor, and I suspect the normalized features would be the issue.
Regarding this issue, can you share you insight?
Dear authors,
I'd like to test your model on my custom 3D reconstructed pointclouds + rgb map.
In order to directly use the checkpoints you shared, I need to extract the normalization features from 3D mesh (as your models are all trained with the norm features).
However, I have no idea how to make the norm features from my custom dataset. I understand the normalized features are calculated in read_mesh_vertices_rgb_normal() from the face of 3D mesh. But I only have 3D pointclouds and rgb, without the mesh faces.
I initially tried to get the faces using Meshlab tool (surface reconstruction - ball pivoting), and the result is as below.
Using the 3D mesh, I followed the all preprocessing steps (xyz, rgb, norm) to extract the similar features to fit your model with the checkpoint(
scanrefer_scst_vote2cap_detr_pp_XYZ_RGB_NORMAL.pth).But the detection and captioning result was really poor, and I suspect the normalized features would be the issue.
Regarding this issue, can you share you insight?