1 vin.encoders.pointnext

vin.encoders.pointnext

Optional PointNeXt-S encoder for semidense VIN point clouds.

This module owns PointNeXtSEncoder, which adapts OpenPoints PointNeXt-S to the VIN encoder interface. It is only constructed when a model config enables the semidense point encoder, so importing aria_nbv.vin.encoders does not build an OpenPoints model or load a checkpoint.

1.1 Classes

Name	Description
PointNeXtSEncoder	Adapt OpenPoints PointNeXt-S to a VIN semidense point-cloud embedding.
PointNeXtSEncoderConfig	Config-as-factory wrapper for PointNeXtSEncoder.

1.1.1 PointNeXtSEncoder

vin.encoders.pointnext.PointNeXtSEncoder(config)

Adapt OpenPoints PointNeXt-S to a VIN semidense point-cloud embedding.

1.1.1.1 Parameters

Name	Type	Description	Default
config	PointNeXtSEncoderConfig	Config-as-factory object containing OpenPoints config/checkpoint paths, output projection dimension, and freezing behavior.	required

1.1.1.2 Notes

The wrapped OpenPoints model is loaded lazily at module construction, not at package import time. Active scorer configs must explicitly own whether this optional path participates in a model.

The adapter makes no stronger symmetry claim than the configured OpenPoints model. Its final spatial mean is order-insensitive, but that alone does not establish graph-isomorphism or rigid-motion invariance for the upstream PointNeXt computation.

1.1.1.3 Methods

Name	Description
train	Switch training mode while keeping frozen OpenPoints weights in eval mode.
forward	Encode point clouds into a compact semidense embedding.

1.1.1.3.1 train

vin.encoders.pointnext.PointNeXtSEncoder.train(mode=True)

Switch training mode while keeping frozen OpenPoints weights in eval mode.

1.1.1.3.2 forward

vin.encoders.pointnext.PointNeXtSEncoder.forward(points)

Encode point clouds into a compact semidense embedding.

1.1.1.3.2.1 Parameters

Name	Type	Description	Default
points	Tensor	Tensor["B N 3", float32] or Tensor["B N C", float32] semidense points in the rig frame. When C > 3, the first three channels are XYZ and the remaining channels are treated as per-point features.	required

1.1.1.3.2.2 Returns

Name	Type	Description
	Tensor	Tensor["B out_dim", float32] semidense embeddings.

1.1.2 PointNeXtSEncoderConfig

vin.encoders.pointnext.PointNeXtSEncoderConfig()

Config-as-factory wrapper for PointNeXtSEncoder.

Paths are resolved against aria_nbv.configs.path_config.PathConfig so model configs can store repository-relative external checkpoint references. The target module expects CUDA tensors because OpenPoints PointNeXt-S uses CUDA-only kernels in the configured runtime.

1.1.2.1 Attributes

Name	Description
target_type	Factory target for aria_nbv.configs.base.BaseConfig.setup_target.
cfg_path	Path to the PointNeXt YAML config, relative to PathConfig.external_dir.
checkpoint_path	Optional pretrained checkpoint (PointNeXt/OpenPoints format).
out_dim	Output embedding dimension produced for the semidense point cloud.
max_points	Subsample semidense points to this count before encoding.
freeze	Whether to freeze PointNeXt-S weights during training.
strict_load	Whether to enforce strict checkpoint loading.