- Add bbox_depth_center position strategy in SceneGraphBuilder using depth
at bbox centre and configurable camera_hfov_degrees for pinhole projection.
- Add optional depth_sensor_uuid to HabitatSimulatorConfig; create depth
sensor spec alongside RGB sensor.
- Add camera_position/camera_rotation fields to RoomView; capture pose from
sensor_states when depth sensor is available.
- Update flatten_room_views for backward compatibility with legacy tuple
format.
- Wired in depth sensor and bbox_depth_center strategy in verification
notebook.
- Add tests for depth sensor support and new position strategies.
Introduce SceneGraphBuilder + SceneGraphBuildConfig to decouple scene graph
construction from the verification notebook. The builder handles batch
inference, hash encoding, and object node creation internally.
- Add SceneGraphBuilder.build_from_room_views() as the main entry point
- Add SceneGraphBuildConfig for inference_batch_size and position strategy
- Add SceneGraphBuildArtifacts to carry cropped images and debug metadata
- Extend ObjectNode with optional detection metadata (label, confidence,
bbox_xyxy, source_view_id, position_confidence)
- Add RoomView frozen dataclass as a structured view container
- Add flatten_room_views() utility to replace inline list comprehensions
- Refactor notebooks/verification.py to use the new builder API
BREAKING CHANGE: ObjectNode now accepts additional optional fields; direct
scene_graph.objects[obj_id] = ObjectNode(...) construction in the notebook
is replaced by builder.build_from_room_views(...).
- Export ImageSceneGraphQueryResult and query_image_against_scene_graph from scenegraph module
- Replace inline hamming-distance-based image matching with dedicated query_image_against_scene_graph function
- Improve top_matches structure by extracting similarity scores and hash_bytes from matches
- Add .codegraph/ to gitignore (machine-local data, should not be committed)
- Add CodeGraph configuration for multi-language indexing
- Add FramePacket dataclass to encapsulate per-image pipeline state
- Rename internal methods with underscore prefix convention
- Replace separate filter_batch/crop_batch with unified process_batch method
- Update notebook to use new HashPipeline API
- Add Owlv2ForObjectDetection and Owlv2Processor imports to model_loader
- Refactor load_dino_model to return tuple of processor and model
- Rewrite generate_proposals_batch to group images by bbox count for efficient batching
- Add _normalize_single_bbox_list helper for bbox normalization
- Update verification.py to use new pipeline architecture with detect/segment/filter/crop steps
- Refactor model_loader.py: improve return type annotations from tuple[Any, Any] to tuple[AutoImageProcessor, AutoModel]
- Refactor proposal/core.py: add input validation for mask array dimensionality, handle 2D masks and batch dimensions gracefully
- Refactor proposal_segament.ipynb: replace inline model loading with centralized load_owlv2_model() and load_sam_model() functions, use batched detect_objects_batch() and generate_proposals_batch() APIs
- Replace SAM2AutomaticMaskGenerator pipeline with Sam2Processor+Sam2Model
- Freeze SAM model parameters at load time, removing torch.no_grad() at call sites
- Rewrite proposal/core.py to use bbox prompts instead of automatic point sampling
- Add bboxes parameter to all HashPipeline public methods (forward, forward_dataset, extract_features, extract_features_dataset)
- Extract mask filtering logic (_filter_masks) from proposal into pipeline
- Rename object_score/ to filter/
- Add load_owlv2_model to model_loader
- Rename notebooks/test.py to habitat_sim_setup.py
