feat: Add advanced loss computation methods to LossManager

- Implemented `eikonal_loss` to compute gradient regularization for non-manifold points
- Added `offsurface_loss` to penalize points far from the surface with near-zero predictions
- Introduced `consistency_loss` to enforce prediction consistency between manifold and non-manifold points

code/conversion/loss.py

@@ -38,6 +38,30 @@ class LossManager:
 
         return self.normals_lambda * normals_loss  # 返回加权后的法线损失
 
+    def eikonal_loss(self, nonmnfld_pnts, nonmnfld_pred_all):
+        """
+        计算Eikonal损失
+        """
+        grad_loss_h = torch.zeros(1).cuda()  # 初始化 Eikonal 损失
+        single_nonmnfld_grad = gradient(nonmnfld_pnts, nonmnfld_pred_all[:,0])  # 计算非流形点的梯度
+        eikonal_loss = ((single_nonmnfld_grad.norm(2, dim=-1) - 1) ** 2).mean()  # 计算 Eikonal 损失
+        return eikonal_loss
+
+    def offsurface_loss(self, nonmnfld_pnts, nonmnfld_pred_all):
+        """
+        Eo
+        惩罚远离表面但是预测值接近0的点
+        """
+        offsurface_loss = torch.exp(-100.0 * torch.abs(nonmnfld_pred_all[:,0])).mean()  # 计算离表面损失
+        return offsurface_loss
+
+    def consistency_loss(self, mnfld_pnts, mnfld_pred_all, all_fi):
+        """
+        惩罚流形点预测值和非流形点预测值不一致的点
+        """
+        mnfld_consistency_loss = (mnfld_pred - all_fi[:,0]).abs().mean()  # 计算流形一致性损失
+        return mnfld_consistency_loss
+
     def compute_loss(self, outputs):
         """
         计算流型损失的逻辑