def boxLossEstimator(self, box_feature, gt_box_feature):
pred_box_pc = transform_pc_batch(self.unit_cube, box_feature)
with torch.no_grad():
pred_reweight = get_surface_reweighting_batch(box_feature[:, 3:6], self.unit_cube.size(0))
gt_box_pc = transform_pc_batch(self.unit_cube, gt_box_feature)
with torch.no_grad():
gt_reweight = get_surface_reweighting_batch(gt_box_feature[:, 3:6], self.unit_cube.size(0))
dist1, dist2 = self.chamferLoss(gt_box_pc, pred_box_pc)
loss1 = (dist1 * gt_reweight).sum(dim=1) / (gt_reweight.sum(dim=1) + 1e-12)
loss2 = (dist2 * pred_reweight).sum(dim=1) / (pred_reweight.sum(dim=1) + 1e-12)
loss = (loss1 + loss2) / 2
return loss
def get_box_loss(self, box_size, quat1, quat2, center1, center2):
box1 = torch.cat([center1, box_size, quat1], dim=-1)
box2 = torch.cat([center2, box_size, quat2], dim=-1)
box1_pc = transform_pc_batch(self.unit_cube, box1)
box2_pc = transform_pc_batch(self.unit_cube, box2)
with torch.no_grad():
box1_reweight = get_surface_reweighting_batch(
box1[:, 3:6], self.unit_cube.size(0))
box2_reweight = get_surface_reweighting_batch(
box2[:, 3:6], self.unit_cube.size(0))
d1, d2 = chamfer_distance(box1_pc, box2_pc, transpose=False)
loss_per_data = (d1 * box1_reweight).sum(dim=1) / (box1_reweight.sum(dim=1) + 1e-12) + \
(d2 * box2_reweight).sum(dim=1) / (box2_reweight.sum(dim=1) + 1e-12)
return loss_per_data