Some Questions Regarding Inference and Model

I have been exploring your work on Sonata and Concerto, and I have a few questions regarding model inference and training details:

1、In PCA, I noticed that the inference model is set to mode=train. Could you please clarify why train is used instead of test, and what the purpose of test mode is?
https://github.com/facebookresearch/sonata/blob/18c09ff8d713494f78a8213792262b910977a65d/sonata/transform.py#L881C8-L881C47

2、You released a version that does not require color or normals https://huggingface.co/Pointcept/Sonata/blob/main/sonata_20251024.pth. How does its performance compare to the version that uses normals? Regarding sonata_20251024.pth, was this version trained on outdoor datasets? Would it be suitable for outdoor environments?

3、The sonata.pth released at https://huggingface.co/facebook/sonata/tree/main
, was it trained on any outdoor datasets?

4、Regarding the newly released Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations, was it trained on both indoor and outdoor data? Can it work using only point cloud data, without requiring color, normals, intensity, or other additional features?

5、Why were versions of Sonata and Concerto that do not use normals released, yet it is said to be a feature of Project Utonia? Is it because the performance of the no-normals versions in Sonata and Concerto is not very good?

Thank you very much for your time and assistance.

Start by reviewing sonata/transform.py at the linked line and the Sonata and Concerto checkpoint pages referenced in the issue. Compare the stated training inputs and datasets for the released models, then document answers to all five questions with supporting project or checkpoint details.

Written by the indexing model from the issue text.