I read the paper yesterday, would recommend it. Kudos to the authors for getting to these results, and also for presenting them in a polished way. It's nice to follow the arguments about the alternating attention (global across all tokens vs only the tokens per camera), the normalization (normalize the scene scale - done in the data vs DUST3R, which normalizes in the network), and the tokens (image tokens from DINOv2 + camera tokens + additional register tokens, handling the first camera differently as it becomes the frame of reference). The results are amazing, and fine-tuning this model will be fun, e.g. for forward 3DGS reconstruction, looking forward to this.

I'm sure getting to this point was quite difficult, and on the project page you can read how it involved discussions with lots and lots of smart and capable people. But there's no big "aha" moment in the paper, so it feels like another hit for The Bitter Lesson in the end: They used a giant bunch of [data], a year and a half of GPU time to [train] the final model, and created a model with a billion parameters that outperforms all specialized previous models.

Or in the words of the authors, from the paper:

> We also show that it is unnecessary to design a special network for 3D reconstruction. Instead, VGGT is based on a fairly standard large transformer [119], with no particular 3D or other inductive biases (except for alternating between frame-wise and global attention), but trained on a large number of publicly available datasets with 3D annotations.

Fantastic to have this. But it feels.. yes, somewhat bitter.

[The Bitter Lesson]: http://www.incompleteideas.net/IncIdeas/BitterLesson.html (often discussed on HN)

[data]: "Co3Dv2 [88], BlendMVS [146], DL3DV [69], MegaDepth [64], Kubric [41], WildRGB [135], ScanNet [18], HyperSim [89], Mapillary [71], Habitat [107], Replica [104], MVS-Synth [50], PointOdyssey [159], Virtual KITTI [7], Aria Synthetic Environments [82], Aria Digital Twin [82], and a synthetic dataset of artist-created assets similar to Objaverse [20]."

[train]: "The training runs on 64 A100 GPUs over nine days", that would be around $18k on lambda labs in case you're wondering