This work proposed a 3D autoencoder architecture, named LiLa-Net, which encodes efficient features from real traffic environments, employing only the LiDAR's point clouds. For this purpose, we have real semi-autonomous vehicle, equipped with Velodyne LiDAR. The system leverage skip connections concept to improve the performance without using extensive resources as the state-of-the-art architectures. Key changes include reducing the number of encoder layers and simplifying the skip connections, while still producing an efficient and representative latent space which allows to accurately reconstruct the original point cloud. Furthermore, an effective balance has been achieved between the information carried by the skip connections and the latent encoding, leading to improved reconstruction quality without compromising performance. Finally, the model demonstrates strong generalization capabilities, successfully reconstructing objects unrelated to the original traffic environment.

翻译：本文提出了一种名为LiLa-Net的三维自编码器架构，该架构仅利用激光雷达的点云数据，从真实交通环境中编码出高效特征。为此，我们使用了一辆配备Velodyne激光雷达的真实半自动驾驶车辆。该系统借鉴跳跃连接的思想，在不使用最先进架构所需的大量计算资源的情况下提升了性能。关键改进包括减少编码器层数并简化跳跃连接，同时仍能生成高效且具有代表性的潜在空间，从而精确重建原始点云。此外，模型在跳跃连接携带的信息与潜在编码之间实现了有效平衡，在不牺牲性能的前提下提高了重建质量。最后，该模型展现出强大的泛化能力，能够成功重建与原始交通环境无关的物体。