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.

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.

In recent years, Neural Radiation Fields (NeRF) have shown tremendous potential in encoding highly-detailed 3D geometry and environmental appearance, thus making it a promising alternative to traditional explicit maps for robot localization. However, current NeRF localization methods suffer from significant computational overheads, primarily resulting from the large number of iterations or particle samples required, as well as the additional computational demands associated with the estimation of the initial pose through multimodal sensors. To overcome these challenges, we propose a novel and time-efficient NeRF localization pipeline, named FVLoc-NeRF. This pipeline solely employs RGB monocular images as input and leverages a retrieval method to obtain the initial pose. Subsequently, the pose update is derived using the Perspective-n-Point (PnP) algorithm, thereby considerably reducing the number of iterations and accelerating the localization process. Our extensive experimental results clearly demonstrate that FVLoc-NeRF is much faster than the state-of-the-art method.

In this paper, we propose an automatic conversion and verification method based on AADL (Architecture Analysis and Design Language) scheduling model for Embedded Real-Time System using timed automata and the Eclipse plug-in development technology. Firstly, according to the proprieties of AADL scheduling models, the Non-preemptive and preemptive scheduler with periodic and aperiodic thread timed automata templates are designed to guide the transformation from components and properties of AADL models to timed automata. Then, the Eclipse plug-in is designed and integrated into OSATE providing an integrated development environment of modeling, conversion, verification and analysis with UPPAAL. Simulated experiments demonstrate that our proposed method can efficiently perform the conversion and verification of the original AADL scheduling model.