Point cloud classification, crucial for discriminative 3D shape analysis, has witnessed significant progress through the application of deep learning. A significant research focus has been on aggregating local point cloud features. A key limitation of previous methods lies in their inherent opacity, making it challenging to understand the underlying reasons for their predictions. Furthermore, these methods frequently exhibit poor generalization performance, struggling to maintain their efficacy when applied to data that deviates from their training distribution. Our method aims to tackle these challenges. We propose ICP-Classifier, a neural-network-free and training-free paradigm that uses Iterative Closest Point (ICP) for classification, which is simple yet robust. The inherent transparency of our method allows for straightforward interpretation of its predictions and underlying mechanisms. By comparing test samples with a reference library, ICP-Classifier predicts labels based on overlap ratios, showing strong generalization on out-of-distribution datasets and robustness facing perturbed data. While capable of achieving high accuracy, our work is exploratory in nature, aiming to explore potential solutions for existing challenges. In contrast to the current focus on complex local feature aggregation, our findings suggest that the inherent shape of 3D objects holds significant discriminative potential, opening up new avenues for exploring robust methods and deepening our understanding of 3D shape analysis.

Mixed autonomy traffic systems face significant security challenges when malicious agents disrupt coordination between autonomous and human-driven vehicles. We present Malicious Agent Detection and Isolation (MADI), a framework addressing two critical forms of disruptive behavior: path order violations at coordination points and strategic congestion generation. MADI integrates dual-mechanism detection with temporal consistency analysis to identify sophisticated malicious behaviors while filtering transient anomalies that could trigger false positives. Upon detection, our framework employs adaptive isolation strategies including enlarged safety boundaries and dynamic priority adjustment. Extensive experiments in simulated highway and urban environments demonstrate that MADI achieves up to 91% detection accuracy with only 4% false positives, significantly outperforming rule-based, anomaly-based, and single-criterion methods. The framework reduces travel time impacts by 25.5% and near-collision events by 76.5% in adversarial conditions, demonstrating its effectiveness for enhancing safety and efficiency in mixed autonomy traffic.

Recent advancements in neural rendering methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D-GS), have significantly revolutionized photo-realistic novel view synthesis of scenes with multiple photos or videos as input. However, existing approaches within the NeRF and 3D-GS frameworks often assume the independence of point sampling and ray casting, which are intrinsic to volume rendering and alpha-blending techniques. These underlying assumptions limit the ability to aggregate context within subspaces, such as densities and colors in the radiance fields and pixels on the image plane, leading to synthesized images that lack fine details and smoothness. To overcome this, we propose a universal framework, MixRF, comprising a Radiance Field Mixer (RF-mixer) and a Color Domain Mixer (CD-mixer), to sufficiently aggregate and fully explore information in neighboring sampled points and casting rays, separately. The RF-mixer treats sampled points as an explicit point cloud, enabling the aggregation of density and color attributes from neighboring points to better capture local geometry and appearance. Meanwhile, the CD-mixer rearranges rendered pixels on the sub-image plane, improving smoothness and recovering fine details and textures. Both mixers employ a kernel-based mixing strategy to facilitate effective and controllable attribute aggregation, ensuring a more comprehensive exploration of radiance values and pixel information. Extensive experiments demonstrate that our MixRF framework is compatible with radiance field-based methods, including NeRF and 3D-GS designs. The proposed framework dramatically enhances performance in both qualitative and quantitative evaluations, with less than a $ 25\%$25% increase in computational overhead during inference.