Existing point cloud feature learning networks often learn high-semantic point features representing the global context by incorporating sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation. However, this process may result in a substantial loss of granular information due to the sampling operation and the widely-used max pooling feature aggregation, which neglects information from non-maximum point features. Consequently, the resulting high-semantic point features could be insufficient to represent the local context, hindering the network’s ability to distinguish fine shapes. To address this problem, we propose PointStack, a novel point cloud feature learning network that utilizes multi-resolution feature learning and learnable pooling (LP). PointStack aggregates point features of various resolutions across multiple layers to capture both high-semantic and high-resolution information. The LP function calculates the weighted sum of multi-resolution point features through an attention mechanism with learnable queries, enabling the extraction of all available information. As a result, PointStack can effectively represent both global and local contexts, allowing the network to comprehend both the global structure and local shape details. PointStack outperforms various existing feature learning networks for shape classification and part segmentation on the ScanObjectNN and ShapeNetPart datasets, achieving 87.2% overall accuracy and instance mIoU.

Four-dimensional (4D) Radar is a useful sensor for the detection of surrounding three-dimensional (3D) objects under various weather conditions. However, since Radar measurements could be corrupted by noise, interference, multipath, and clutter, it is necessary to employ a preprocessing algorithm to filter out invalid measurements before the object detection with neural networks. In this paper, we propose RTNH+ that is an enhanced version of RTNH, a 4D Radar object detection network, based on two novel proposed algorithms. The first algorithm is the two-level preprocessing (TLP) algorithm that produces two measurement outputs from the same 4D Radar measurements at different filtering levels. The two outputs enrich the representation of the 4D Radar measurements and enhance object detection network performance. The second is the vertical encoding (VE) algorithm that effectively encodes vertical features of the surrounding objects in the TLP outputs. We provide details of the RTNH+ and demonstrate that RTNH+ achieves significant performance improvement of 10.14% in ${{AP}_{3D}^{IoU=0.3}}$ and 16.12% in ${{AP}_{3D}^{IoU=0.5}}$ over RTNH.The codes are available at https://github.com/kaist-avelab/K-Radar

Utilizing the collected experience tuples in the replay buffer (RB) is the primary way of exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and, therefore, the sampling scheme for the experience tuples in the RB can be critical for experience utilization. In this paper, it is found that a widely used sampling scheme in the off-policy RL suffers from inefficiency due to the inadequate uneven sampling of experience tuples from the RB. In fact, the conventional uniform sampling of the experience tuples in the RB causes a severely unbalanced experience utilization, since experiences stored earlier in the RB is sampled with much higher frequency especially in the early stage of learning. We mitigate this fundamental problem by employing a half-normal sampling probability window that allocates a higher sampling probability to newer experiences in the RB. In addition, we propose general and local size adjustment schemes that determine the standard deviation of the half-normal sampling window to enhance the learning speed and performance and to mitigate the temporary performance degradation during training, respectively. For performance demonstration, we apply the proposed sampling technique to the state-of-the-art off-policy RL algorithms and test for various RL benchmark tasks such as MuJoCo gym and CARLA simulator. As a result, the proposed technique shows considerable learning speed and final performance improvement, especially on the tasks with large state and action space. Furthermore, the proposed sampling technique increases the stability of the considered RL algorithms, verified with less variance of the performance results across different random seeds of network initialization.

Lane detection algorithm (LDA) is a crucial and necessary component for autonomous vehicles to ensure safe driving in various environments. Deep learning-based lane detection algorithms (DL-LDAs) have gained significant attention recently, and there have been a number of DL-LDAs, introduced in the literature, showing a continuous performance improvement in lane detection. In general, DL-LDAs are composed of pre-processing, lane feature extraction, lane detection head, and an optional lane fitting. For a systematic overview of various DL-LDAs, we provide detailed explanations for each functional component of DL-LDAs. Moreover, this paper presents the first survey to comprehensively analyze various DL-LDAs using camera and LiDAR, such as 2D (2-Dimensional) and 3D DL-LDAs using camera images and DL-LDAs using LiDAR point cloud or sensor fusion. In addition to the analysis, we present recent public lane detection benchmarks for DL-LDAs and discussions concerning technical issues that need to be addressed in future DL-LDA studies.