# Point Transformer V3 encoder (negative result)

This is the Point Transformer V3 encoder experiment from the write-up. We
replaced the Perceiver encoder with a Point Transformer V3 encoder (per-point
features, no latent bottleneck) to test whether a stronger encoder would scale
past the 8k Perceiver.

**Outcome.** Trained from scratch at 8k for 200k steps, it plateaued at ~0.323
local HSS, below the curriculum-trained Perceiver (~0.357), and ran at roughly
6 s/sample on an A5000 — over the two-hour T4 evaluation budget. It was never
submitted. This folder is provided as confirmation that the experiment was run.

## Contents

```
checkpoint_ptv3_8k.pt     trained weights (200k steps at 8k)
train_args.json           training configuration
ptv3_code/                the PT v3 encoder (adapted from the Pointcept release)
                          and the [B,T,*] <-> flat adapter (encoder_wrapper.py)
model_with_ptv3.py        EdgeDepthSegmentsModel with the arch="ptv3" branch
                          (drop-in replacement for s23dr_2026_example/model.py)
```

## Notes

- Extra dependencies beyond the main `requirements.txt`:
  `spconv-cu121`, `torch-scatter`, `addict` (and optionally `flash-attn`).
- To run it, place `ptv3_code/` as `s23dr_2026_example/ptv3/`, use
  `model_with_ptv3.py` as `s23dr_2026_example/model.py`, and load the checkpoint
  with `arch="ptv3"`. Inference must use fp32 (spconv does not tune fp16 kernels
  reliably on these GPUs).
- The PT v3 encoder is adapted from the authors' Pointcept release.