Advancements in lidar systems have improved the performance of 3D data acquisition. Differences arise between the point clouds obtained by different lidar sensors, such as variations in point density, random error, and scanning patterns. This study presents a novel approach for automatic cross-sensor matching of lidar point clouds using a deep neural network autoencoder (DNN-AE) and supervoxel signatures. A compact representation called a supervoxel signature was formed by voxelizing and reprojecting the point clouds, generating multiscale supervoxels, and encoding them with a DNN-AE. The proposed method demonstrated high matching accuracy and tolerance to point density differences and random registration, showcasing its effectiveness in addressing the challenges associated with varying lidar sensor data. From the simulation results, the supervoxel signature had a matching correctness of 83.78% when the point density was 1/256 of the original one, and the tolerance to random errors reached the submeter level. In addition, the multiscale supervoxel signature was more reliable than the single-scale combination. In real-world cross-sensor experiments involving consumer-grade and surveying-grade lidar systems, the proposed method achieved a matching accuracy exceeding 90% by aggregating features across adjacent frames, while significantly reducing data volume. These results confirm the robustness and practicality of the proposed framework for reliable and efficient heterogeneous point cloud matching.