NVPanoptix3D

NVPanoptix3D for panoptic 3D scene reconstruction from posed RGB images. Produces 3D panoptic segmentation (semantic, instance, and panoptic masks) with occupancy completion. Built on VGGT backbone with Mask2Former-style head and 3D frustum reconstruction.

Uses 2D and 3D stage checkpoints. Set train.checkpoint_2d and train.checkpoint_3d for staged initialization.

Dataclass Schemas

Generated TAO Core schemas are packaged in

schemas/<action>.schema.json

, with

schemas/manifest.json

listing available actions. Each generated schema also emits

references/spec_template_<action>.yaml

from the schema top-level

default

field. AutoML enablement is declared at the model layer in

references/skill_info.yaml

via

automl_enabled

. Runnable AutoML still requires

schemas/train.schema.json

and

references/spec_template_train.yaml

to exist and parse. Use the packaged train schema for

automl_default_parameters

automl_disabled_parameters

, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect

~/tao-core

at runtime; maintainers regenerate schemas/templates before packaging the skill bank.

Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read

references/skill_info.yaml

and resolve the run override from either an explicit

automl_policy

value or the user's workflow request. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as

automl_policy: off

for this run only; otherwise default to

auto

. When

automl_policy: auto

automl_enabled: true

, and both

schemas/train.schema.json

and

references/spec_template_train.yaml

are packaged, route the train action through

tao-skill-bank:tao-run-automl

by default with this model's

skill_dir

. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and

automl_policy

. Use direct model training only when

automl_policy: off

or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.

Non-train actions such as

evaluate

inference

export

, and deploy flows stay in this model skill. The per-run

automl_policy

override does not change model metadata.

Training Requirements

Dataset type: nvpanoptix3d
Formats: front3d, matterport
Monitoring metric: kpi

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	dataset.frustum_mask_path	eval_dataset	meta/frustum_mask.npz	No
evaluate	dataset.label_map	eval_dataset	meta/colormap.json	No
evaluate	dataset.val.json_path	eval_dataset	meta/val.json	No
evaluate	dataset.val.base_dir	eval_dataset		No
evaluate	dataset.test.json_path	inference_dataset	meta/test.json	No
evaluate	dataset.test.base_dir	inference_dataset		No
inference	dataset.frustum_mask_path	inference_dataset	meta/frustum_mask.npz	No
inference	dataset.label_map	inference_dataset	meta/colormap.json	No
inference	inference.images_dir	inference_dataset	images.tar.gz	No
train	dataset.frustum_mask_path	train_datasets	meta/frustum_mask.npz	No
train	dataset.label_map	train_datasets	meta/colormap.json	No
train	dataset.train.json_path	train_datasets	meta/train.json	No
train	dataset.train.base_dir	train_datasets		No
train	dataset.val.json_path	eval_dataset	meta/val.json	No
train	dataset.val.base_dir	eval_dataset		No
train	dataset.test.json_path	inference_dataset	meta/test.json	No
train	dataset.test.base_dir	inference_dataset		No

Typical Spec Overrides

Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in

spec_overrides

python

S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

python

{
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.enable_3d": True,
    "model.sem_seg_head.num_classes": 13,
    "dataset.frustum_mask_path": f"{S3_TRAIN}/meta/frustum_mask.npz",
    "dataset.label_map": f"{S3_TRAIN}/meta/colormap.json",
    "dataset.train.json_path": f"{S3_TRAIN}/meta/train.json",
    "dataset.train.base_dir": f"{S3_TRAIN}",
    "dataset.val.json_path": f"{S3_EVAL}/meta/val.json",
    "dataset.val.base_dir": f"{S3_EVAL}",
    "dataset.test.json_path": f"{S3_EVAL}/meta/test.json",
    "dataset.test.base_dir": f"{S3_EVAL}",
}

evaluate (mandatory data sources):

python

{
    "dataset.enable_3d": True,
    "dataset.frustum_mask_path": f"{S3_EVAL}/meta/frustum_mask.npz",
    "dataset.label_map": f"{S3_EVAL}/meta/colormap.json",
    "dataset.val.json_path": f"{S3_EVAL}/meta/val.json",
    "dataset.val.base_dir": f"{S3_EVAL}",
    "dataset.test.json_path": f"{S3_EVAL}/meta/test.json",
    "dataset.test.base_dir": f"{S3_EVAL}",
}

inference (mandatory data sources):

python

{
    "dataset.enable_3d": True,
    "dataset.frustum_mask_path": f"{S3_EVAL}/meta/frustum_mask.npz",
    "dataset.label_map": f"{S3_EVAL}/meta/colormap.json",
    "inference.images_dir": f"{S3_EVAL}/images.tar.gz",
}

Eval Dataset

Optional. Val/test splits configured via dataset.val and dataset.test paths.

Important Parameters

model.sem_seg_head.num_classes: Number of semantic classes. Default 13.
model.mode: Prediction mode. Options: panoptic, instance, semantic. Default panoptic.
model.backbone_type: Backbone. Default vggt (only option in schema).
model.mask_former.num_object_queries: Object queries. Default 100.
model.mask_former.dec_layers: Decoder layers. Default 10.
model.frustum3d.truncation: 3D frustum truncation. Default 3.
model.frustum3d.panoptic_weight: Panoptic loss weight. Default 25.
model.frustum3d.completion_weights: Completion loss weights. Default [50, 25, 10].
dataset.name: Dataset name. Options: front3d, matterport, synthetic_hospital, synthetic_warehouse.
dataset.downsample_factor: Image downsample factor. Default 1 (Front3D), 2 (Matterport).
dataset.target_size: Target image size. Default [320, 240].
dataset.depth_min: Min depth. Default 0.4 meters.
dataset.depth_max: Max depth. Default 6.0 meters.
train.lr: Learning rate. Default 2e-4. backbone_multiplier=0.1.
train.lr_scheduler: Options: MultiStep, Warmuppoly. Milestones [88, 96].
train.precision: Options: fp16, fp32. Default fp16.
train.distributed_strategy: Options: ddp, fsdp. activation_checkpoint=True by default.
train.clip_grad_norm: Gradient clipping norm. Default 0.1.
export.onnx_file_2d: ONNX path for 2D model component.
export.onnx_file_3d: ONNX path for 3D model component.
export.max_voxels: Max voxels for engine input. Default 700000.
inference.mode: Options: semantic, instance, panoptic.

Multi-GPU / Multi-Node

Launch method: Lightning-managed (single

python

process, Lightning spawns workers).

Spec Key	Description	Default
`train.num_gpus`	Number of GPUs	1
`train.gpu_ids`	GPU device indices	[0]
`train.num_nodes`	Number of nodes	1
`train.distributed_strategy`	`ddp` only	`ddp`

fsdp
is NOT supported for NVPanoptix3D (code only handles
```
ddp
```
)
```
ddp
```
with activation checkpointing (enabled by default):
```
find_unused_parameters=False
```
```
ddp
```
without:
```
find_unused_parameters=True
```
FAN backbones with 3D enabled auto-enable
```
sync_batchnorm
```

Multi-node env vars (set by orchestrator):

WORLD_SIZE

NODE_RANK

MASTER_ADDR

MASTER_PORT

NUM_GPU_PER_NODE

Export / TRT Defaults

Exports separate 2D and 3D ONNX models (onnx_file_2d, onnx_file_3d)
TRT data types: FP32, FP16 only
max_voxels: 700000 (engine input tensor limit)

Hardware

Minimum 2 GPU(s), recommended 4 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. 3D reconstruction is very memory intensive. fp16 recommended. activation_checkpoint enabled by default. FSDP for multi-node. AutoML is enabled at the model layer; preserve this GPU/VRAM guidance when routing train through AutoML.

Error Patterns

Missing frustum mask: Ensure meta/frustum_mask.npz is present in the dataset directory.

Downsample factor mismatch: Use downsample_factor=2 for Matterport3D, 1 for Front3D / synthetic datasets.

3D occupancy OOM: Reduce frustum_dims or grid_dimensions if running out of GPU memory during 3D reconstruction.

Spec Param / Parent Model Inference

Model-specific inference mappings belong in this MD file, not in

config.json

. Generated runners should read this section and apply the mappings with SDK helpers before

create_job()

. This mirrors the old microservices

infer_params.py

flow.

Inference mappings from TAO Core

nvpanoptix3d.config.json

Action	Spec Field	Inference Function	Meaning
evaluate	`encryption_key`	`key`	encryption key
evaluate	`evaluate.checkpoint`	`parent_model`	model file inferred from the parent job results folder
evaluate	`results_dir`	`output_dir`	current job results directory
export	`encryption_key`	`key`	encryption key
export	`export.checkpoint`	`parent_model`	model file inferred from the parent job results folder
export	`export.onnx_file_2d`	`create_onnx_file_2d`	create_onnx_file_2d
export	`export.onnx_file_3d`	`create_onnx_file_3d`	create_onnx_file_3d
export	`results_dir`	`output_dir`	current job results directory
inference	`encryption_key`	`key`	encryption key
inference	`inference.checkpoint`	`parent_model`	model file inferred from the parent job results folder
inference	`results_dir`	`output_dir`	current job results directory
train	`encryption_key`	`key`	encryption key
train	`results_dir`	`output_dir`	current job results directory
train	`train.checkpoint_2d`	`parent_model_or_ptm`	parent model if available, otherwise PTM
train	`train.checkpoint_3d`	`ptm`	pretrained model
train	`train.resume_training_checkpoint_path`	`resume_model`	model file inferred from the current job results folder

For

parent_model

parent_model_folder

, pass the upstream train/export/AutoML child job id as

parent_job_id

. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to

config.json

and do not patch generated runner scripts to guess checkpoint paths.

tao-train-nvpanoptix3d

NPX Install

Tags

SKILL.md Content

NVPanoptix3D

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Export / TRT Defaults

Hardware

Error Patterns

Spec Param / Parent Model Inference