VoteNet

VoteNet detection loss: deep Hough voting target assignment and multi-task objective.

Classes:

VoteNetLoss –

Multi-task VoteNet detection loss (vote, objectness, box, semantic).

VoteNetLoss ¶

VoteNetLoss(
    num_heading_bin: int,
    num_size_cluster: int,
    num_classes: int,
    mean_sizes: Union[Tensor, List[List[float]]],
    *,
    near_threshold: float = 0.3,
    far_threshold: float = 0.6,
    objectness_weights: Tuple[float, float] = (0.2, 0.8),
    loss_scale: float = 10.0,
)

Bases: Module

Multi-task VoteNet detection loss (vote, objectness, box, semantic).

Reference: Qi et al., 2019.

Proposals are matched to ground-truth objects by nearest center: a proposal is positive when its nearest GT center is within near_threshold, negative beyond far_threshold, and ignored in the band between. Positives drive the center, heading, size and semantic terms; the vote term pulls each object seed's vote toward its object center (the closest of up to three candidate votes).

Parameters:

num_heading_bin (int) –

Number of heading-angle bins (\(1\) for axis-aligned ScanNet, \(12\) for SUN RGB-D).
num_size_cluster (int) –

Number of size templates.
num_classes (int) –

Number of semantic classes.
mean_sizes (Union[Tensor, List[List[float]]]) –

Per-template mean box size, shape \((\text{num\_size\_cluster}, 3)\).
near_threshold (float, default: 0.3 ) –

Distance (meters) below which a proposal is a positive object match.
far_threshold (float, default: 0.6 ) –

Distance (meters) above which a proposal is a negative match.
objectness_weights (Tuple[float, float], default: (0.2, 0.8) ) –

Cross-entropy class weights \([\text{negative}, \text{positive}]\).
loss_scale (float, default: 10.0 ) –

Global multiplier applied to the summed loss.

Methods:

forward –

Compute the VoteNet loss and its components.

forward ¶

forward(
    output: Dict[str, Tensor], batch: Dict[str, Any]
) -> Dict[str, Tensor]

Compute the VoteNet loss and its components.

Parameters:

output (Dict[str, Tensor]) –

The model's raw output: dense head tensors (objectness_scores, center, heading_scores, heading_residuals_normalized, size_scores, size_residuals_normalized, sem_cls_scores, pos_vote_aggr) as \((B, K, \cdot)\), plus the packed pos_seed, pos_vote \((S, 3)\) and seed_indices, batch_seed, batch_vote \((S,)\).
batch (Dict[str, Any]) –

Ground truth (center_label, heading_class_label, heading_residual_label, size_class_label, size_residual_label, sem_cls_label, box_label_mask as \((B, M, \cdot)\), per-point vote_label \((B, N, 9)\), vote_label_mask \((B, N)\), and the per-point batch index). The heading labels are binned from counter-clockwise headings and re-binned internally into the model's native (negated) heading space.

Returns:

Dict[str, Tensor] –

A dict with the scalar loss (to backprop) and detached vote_loss, objectness_loss,
Dict[str, Tensor] –

box_loss, center_loss, heading_cls_loss, heading_res_loss, size_cls_loss,
Dict[str, Tensor] –

size_res_loss, sem_cls_loss and obj_acc diagnostics.