Bench3DS³ checkpoints

Evaluated 23-class industrial semantic-segmentation weights for Bench3DS³.

Weights are here. Training configs stay in the Git repo (configs/models/). These files are slim Pointcept checkpoints (epoch, state_dict, best_metric_value; VoLT also keeps ema_state_dict). Optimizer / scheduler states are stripped, so train.sh -r true resume is not supported. test.sh -w inference and train.sh -w fine-tuning are.

Repo: https://gitlab.lrz.de/bench3ds3/isaac_sim_data_collector
Docs: docs/usage/models/checkpoints_en.md

Methods

Method	File	Config	Exp dir	Docker	Val epoch / metric
PTv3	`ptv3-base/model.pth`	`semseg-pt-v3m1-0-base`	`ptv3_base_23cls_gpu1`	pointcept	47 / 0.9064272889718135
LitePT	`litept-small/model.pth`	`semseg-litept-v1m1-0-small`	`litept_small_23cls_gpu1`	pointcept	68 / 0.9161536735904534
OA-CNN	`oacnn-base/model.pth`	`semseg-oacnn-v1m1-0-base`	`oacnn_base_23cls_gpu1_run_2`	pointcept	93 / 0.8989892319211662
OctFormer	`octformer-base/model.pth`	`semseg-octformer-v1m1-0-base`	`octformer_base_23cls_gpu1`	pointcept	100 / 0.7835706366509076
PTv3+CAC	`ptv3-cac/model.pth`	`semseg-cac-v1m1-0-base`	`semseg-cac-v1m1-0-base`	pointcept	50 / 0.8942121746552055
Sonata+Linear	`sonata-lin/model.pth`	`semseg-sonata-industrial-lin`	`sonata_industrial_lin`	pointcept	112 / 0.788238835478426
Sonata+FT	`sonata-ft/model.pth`	`semseg-sonata-industrial-ft`	`sonata_industrial_ft`	pointcept	112 / 0.9101897111058175
DiTR	`ditr-aligned/model.pth`	`semseg-pt-v3m1-0-image`	`injection_aligned_23cls_gpu1`	ditr	58 / 0.8728377743924077
VoLT	`volt-small-ft/model.pth`	`semseg-volt-small-ft`	`semseg-volt-small-ft`	volt	98 / 0.9250934182924747

The validation metric is the training-run best_metric_value, not a paper test-set number. Reproduce with the GitLab configs before citing.

Download one method

pip install -U huggingface_hub
hf download min99ian/bench3ds3-checkpoints ptv3-base/model.pth --local-dir ./weights

Place it where Pointcept/DiTR/Volt expect model_best.pth:

mkdir -p exp/industrial/ptv3_base_23cls_gpu1/model
cp weights/ptv3-base/model.pth exp/industrial/ptv3_base_23cls_gpu1/model/model_best.pth

Test

sh scripts/test.sh -g 1 -d industrial \
  -c semseg-pt-v3m1-0-base \
  -n ptv3_base_23cls_gpu1 \
  -w model_best

Fine-tune

train.sh -w loads state_dict and starts a new run (new optimizer). Copy the config from the Git repo, point data_root at your set, use a new -n, and pass the downloaded file to -w:

sh scripts/train.sh -g 1 -d industrial \
  -c semseg-pt-v3m1-0-base \
  -n ptv3_ft_mydataset \
  -w /path/to/weights/ptv3-base/model.pth

DiTR still needs the frozen DINOv2-small encoder at runtime (not stored in the task checkpoint). VoLT test.sh loads state_dict; ema_state_dict is included for completeness.

Classes (train-id order)

barcode, bracket, cardboard_box, cnc_machine, container, conveyor,
fire_extinguisher, floor, floor_decal, forklift, lamp, pallet,
pallet_trolley, pillar, rack, robot, robot_stand, safety_fence, shelf,
sign, table, wall, workpiece

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