vis4d.op.track3d.cc_3dt
CC-3DT graph.
Functions
|
Convert camera coordinates to global coordinates. |
|
Get track 3D output. |
Classes
|
Data association relying on quasi-dense instance similarity and 3D clue. |
- get_track_3d_out(boxes_3d, class_ids, scores_3d, track_ids)[source]
Get track 3D output.
- Parameters:
boxes_3d (Tensor) – (N, 12): x,y,z,h,w,l,rx,ry,rz,vx,vy,vz
class_ids (Tensor) – (N,)
scores_3d (Tensor) – (N,)
track_ids (Tensor) – (N,)
- Returns:
output
- Return type:
- class CC3DTrackAssociation(init_score_thr=0.8, obj_score_thr=0.5, match_score_thr=0.5, nms_backdrop_iou_thr=0.3, nms_class_iou_thr=0.7, nms_conf_thr=0.5, with_cats=True, with_velocities=False, bbox_affinity_weight=0.5)[source]
Data association relying on quasi-dense instance similarity and 3D clue.
This class assigns detection candidates to a given memory of existing tracks and backdrops. Backdrops are low-score detections kept in case they have high similarity with a high-score detection in succeeding frames.
- __init__(init_score_thr=0.8, obj_score_thr=0.5, match_score_thr=0.5, nms_backdrop_iou_thr=0.3, nms_class_iou_thr=0.7, nms_conf_thr=0.5, with_cats=True, with_velocities=False, bbox_affinity_weight=0.5)[source]
Creates an instance of the class.
- Parameters:
init_score_thr (float) – Confidence threshold for initializing a new track.
obj_score_thr (float) – Confidence treshold s.t. a detection is considered in the track / det matching process.
match_score_thr (float) – Similarity score threshold for matching a detection to an existing track.
nms_backdrop_iou_thr (float) – Maximum IoU of a backdrop with another detection.
nms_class_iou_thr (float) – Maximum IoU of a high score detection with another of a different class.
nms_conf_thr (float) – Confidence threshold for NMS.
with_cats (bool) – If to consider category information for tracking (i.e. all detections within a track must have consistent category labels).
with_velocities (bool) – If to use predicted velocities for matching.
bbox_affinity_weight (float) – Weight of bbox affinity in the overall affinity score.
- depth_ordering(obsv_boxes_3d, obsv_velocities, memory_boxes_3d_predict, memory_boxes_3d, memory_velocities)[source]
Depth ordering matching.
- Return type:
Tensor
- __call__(detections, camera_ids, detection_scores, detections_3d, detection_scores_3d, detection_class_ids, detection_embeddings, obs_velocities=None, memory_boxes_3d=None, memory_track_ids=None, memory_class_ids=None, memory_embeddings=None, memory_boxes_3d_predict=None, memory_velocities=None, with_depth_confidence=True)[source]
Process inputs, match detections with existing tracks.
- Parameters:
detections (Tensor) – [N, 4] detected boxes.
camera_ids (Tensor) – [N,] camera ids.
detection_scores (Tensor) – [N,] confidence scores.
detections_3d (Tensor) – [N, 7] detected boxes in 3D.
detection_scores_3d (Tensor) – [N,] confidence scores in 3D.
detection_class_ids (Tensor) – [N,] class indices.
detection_embeddings (Tensor) – [N, C] appearance embeddings.
obs_velocities (Tensor | None) – [N, 3] velocities of detections.
memory_boxes_3d (Tensor) – [M, 7] boxes in memory.
memory_track_ids (Tensor) – [M,] track ids in memory.
memory_class_ids (Tensor) – [M,] class indices in memory.
memory_embeddings (Tensor) – [M, C] appearance embeddings in memory.
memory_boxes_3d_predict (Tensor) – [M, 7] predicted boxes in memory.
memory_velocities (Tensor) – [M, 7] velocities in memory.
- Returns:
- track ids of active tracks and selected
detection indices corresponding to tracks.
- Return type:
tuple[Tensor, Tensor]