Title: Contrastive Adversarial Loss for Point Cloud Reconstruction. Abstract: For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. The Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two widely-used reconstruction losses, which firstly use predefined strategies to match points in two point clouds and then apply the average distances from points to their matched neighbors as differentiable measurements of shape differences. However, the predefined matching rules may deviate from the real shape differences and cause defective reconstructed results. To solve the above problem, we propose a learning-based Contrastive adversarial Loss (CALoss) to train a reconstruction-related task network without the predefined matching rules. CALoss learns to evaluate shape differences by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations.