Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they usually utilized heavy correlation operations to capture category-level characteristics only, and overlook the inherent merit of arbitrariness in contrast to multiple object tracking. In this work, we propose a radically novel one-stream network with the strength of the instance-level encoding, which avoids the correlation operations occurring in previous Siamese network, thus considerably reducing the computational effort. In particular, the proposed method mainly consists of a Template-aware Transformer Module (TTM) and a Multi-scale Feature Aggregation (MFA) module capable of fusing spatial and semantic information. The TTM stitches the specified template and the search region together and leverages an attention mechanism to establish the information flow, breaking the previous pattern of independentextraction-and-correlation. As a result, this module makes it possible to directly generate template-aware features that are suitable for the arbitrary and continuously changing nature of the target, enabling the model to deal with unseen categories. In addition, the MFA is proposed to make spatial and semantic information complementary to each other, which is characterized by reverse directional feature propagation that aggregates information from shallow to deep layers. Extensive experiments on KITTI and nuScenes demonstrate that our method has achieved considerable performance not only for class-specific tracking but also for class-agnostic tracking with less computation and higher efficiency.

The widespread deployment of deep neural networks (DNNs) for 3D point cloud processing contrasts sharply with their vulnerability to security breaches, particularly backdoor attacks. Studying these attacks is crucial for raising security awareness and mitigating potential risks. However, the irregularity of 3D data and the heterogeneity of 3D DNNs pose unique challenges. Existing methods frequently fail against basic point cloud preprocessing or require intricate manual design. Exploring simple, imperceptible, effective, and difficult-to-defend triggers in 3D point clouds remains challenging. To address this issue, we propose iBA, a novel solution utilizing a folding-based auto-encoder (AE). By leveraging united reconstruction losses, iBA enhances both effectiveness and imperceptibility. Its data-driven nature eliminates the need for complex manual design, while the AE core imparts significant nonlinearity and sample specificity to the trigger, rendering traditional preprocessing techniques ineffective. Additionally, a trigger smoothing module based on spherical harmonic transformation (SHT) allows for controllable intensity. We also discuss potential countermeasures and the possibility of physical deployment for iBA as an extensive reference. Both quantitative and qualitative results demonstrate the effectiveness of our method, achieving state-of-the-art attack success rates (ASR) across a variety of victim models, even with defensive measures in place. iBA’s imperceptibility is validated with multiple metrics as well.

Single object tracking in point clouds has been attracting more and more attention owing to the presence of LiDAR sensors in 3D vision. However, existing methods based on deep neural networks mainly focus on training different models for different categories, which makes them unable to perform well in real-world applications when encountering classes unseen during the training phase. In this work, we investigate a more challenging task in LiDAR point clouds, namely class-agnostic tracking, where a general model is supposed to be learned to handle targets of both observed and unseen categories. In particular, we first investigate the class-agnostic performance of state-of-the-art trackers by exposing the unseen categories to them during testing. It is found that as the distribution shifts from observed to unseen classes, how to constrain the fused features between the template and the search region to maintain generalization is a key factor in class-agnostic tracking. Therefore, we propose a feature decorrelation method to address this problem, which eliminates the spurious correlations of the fused features through a set of learned weights, and further makes the search region consistent among foreground points and distinctive between foreground and background points. Experiments on KITTI and NuScenes demonstrate that the proposed method can achieve considerable improvements by benchmarking against the advanced trackers P2B and BAT, especially when tracking unseen objects.

Three-dimensional (3-D) tracking in point cloud is a core competence of autonomous robots to perceive and forecast the environment. How to initialize bounding box seeds and optimize their position and orientation are very crucial for 3-D object tracking in point clouds. Nevertheless, existing methods mainly resort to developing a powerful classifier based on the initial bounding box seeds. In this article, we propose an end-to-end deep supervised descent method (SDM), which seamlessly integrates multiple seeds generation for the initialization of seeds and sequential updates for the estimation of accurate result. Specifically, we start with transforming the SDM iterative process into a trainable recurrent module. It explicitly learns a series of descent directions in the parameter space, to gradually optimize the initial seeds. Moreover, to alleviate drifting of this process, we initialize multiple seeds based on aggregated point sets generated by the deep Hough voting. Besides, a discrimination module is introduced to determine the bounding box with the highest score as the final result. Importantly, a specific multitask loss is proposed to train our model in an end-to-end way. Experiments on KITTI, PandaSet, and Waymo datasets show that our method could achieve significant improvements (up to 11.2% in success ratio) as compared to state-of-the-art trackers.

Designing an effective and uniform framework to meliorate tracking performance is very meaningful and essential. However, existing methods merely focus on single positive and negative instances corresponding to the exemplar, thoroughly ignoring the effective information hidden in other instances. To tackle this issue, in this paper, we present an informative enhanced loss based Siamese tracking network. Specifically, we introduce an informative enhanced loss to enable the network to capture information from an overall perspective. In other words, we construct dense connections among instances and exemplar. More importantly, we prove that our proposed loss can be transformed into the logistic loss and the triplet loss under particular parameter settings. Experiments on prevalent benchmarks demonstrate that the Siamese frameworks trained with our proposed loss indeed obtain better tracking results than original ones, and achieve promising performance against several state-of-the-art trackers on the real-time challenge.

Offsetting-based hollowing is a solid modeling operation widely used in 3D printing, which can change the model's physical properties and reduce the weight by generating voids inside a model. However, a hollowing operation can lead to additional supporting structures for fabrication in interior voids, which cannot be removed. As a consequence, the result of a hollowing operation is affected by these additional supporting structures when applying the operation to optimize physical properties of different models. This paper proposes a support-free hollowing framework to overcome the difficulty of fabricating voids inside a solid. The challenge of computing a support-free hollowing is decomposed into a sequence of shape optimization steps, which are repeatedly applied to interior mesh surfaces. The optimization of physical properties in different applications can be easily integrated into our framework. Comparing to prior approaches that can generate support-free inner structures, our hollowing operation can reduce more volume of material and thus provide a larger solution space for physical optimization. Experimental tests are taken on a number of 3D models to demonstrate the effectiveness of this framework.