Efficient and Probabilistic Adaptive VoxelnMapping for Accurate Online3D SLAM

This paper proposes an efficient and probabilistic adaptive voxel mapping method for 3D SLAM. An accurate uncertainty model of point and plane is proposed for probabilistic plane representation. We analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the voxel map to the iterated Kalman filter and construct the maximum posterior probability problem for pose estimation. The experiments on the open KITTI dataset show the high accuracy and efficiency of our method in contrast with other state-of-the-art. Outdoor experiments on unstructured environments with non-repetitive scanning LiDAR further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns.

Fast and Accurate Extrinsic Calibration for Multiple LiDARs and Cameras

In this letter, we propose a fast, accurate, and targetless extrinsic calibration method for multiple LiDARs and cameras based on adaptive voxelization. On the theory level, we incorporate the LiDAR extrinsic calibration with the bundle adjustment method. We derive the second-order derivatives of the cost function w.r.t. the extrinsic parameter to accelerate the optimization. On the implementation level, we apply the adaptive voxelization to dynamically segment the LiDAR point cloud into voxels with non-identical sizes, and reduce the computation time in the process of feature correspondence matching. The robustness and accuracy of our proposed method have been verified with experiments in outdoor test scenes under multiple LiDAR-camera configurations.

PPT: Pre-trained Prompt Tuning for Few-shot Learning

Prompts for pre-trained language models (PLMs) have shown remarkable performance by bridging the gap between pre-training tasks and various downstream tasks. Among these methods, prompt tuning, which freezes PLMs and only tunes soft prompts, provides an efficient and effective solution for adapting large-scale PLMs to downstream tasks. However, prompt tuning is yet to be fully explored. In our pilot experiments, we find that prompt tuning performs comparably with conventional full-model fine-tuning when downstream data are sufficient, whereas it performs much worse under few-shot learning settings, which may hinder the application of prompt tuning in practice. We attribute this low performance to the manner of initializing soft prompts. Therefore, in this work, we propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. We name this Pre-trained Prompt Tuning framework PPT. To ensure the generalization of PPT, we formulate similar classification tasks into a unified task form and pre-train soft prompts for this unified task. Extensive experiments show that tuning pre-trained prompts for downstream tasks can reach or even outperform full-model fine-tuning under both full-data and few-shot settings. Our approach is effective and efficient for using large-scale PLMs in practice.

Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes

This paper considers joint analysis of multiple functionally related structures in classification tasks. In particular, our method developed is driven by how functionally correlated brain structures vary together between autism and control groups. To do so, we devised a method based on a novel combination of (1) non-Euclidean statistics that can faithfully represent non-Euclidean data in Euclidean spaces and (2) a non-parametric integrative analysis method that can decompose multi-block Euclidean data into joint, individual, and residual structures. We find that the resulting joint structure is effective, robust, and interpretable in recognizing the underlying patterns of the joint variation of multi-block non-Euclidean data. We verified the method in classifying the structural shape data collected from cases that developed and did not develop into Autistic Spectrum Disorder (ASD).

Origin of Charge Density Wave in Layered Kagome Metal CsV$_3$Sb$_5$

Using first-principles calculations, we identify the origin of the observed charge density wave (CDW) formation in a layered kagome metal CsV$_3$Sb$_5$. It is revealed that the structural distortion of kagome lattice forming the trimeric and hexameric V atoms is accompanied by the stabilization of quasimolecular states, which gives rise to the opening of CDW gaps for the V-derived multibands lying around the Fermi level. This Jahn-Teller-like instability having the local lattice distortion and its derived quasimolecular states is a driving force of the CDW order. Specifically, the saddle points of multiple Dirac bands near the Fermi level, located at the $M$ point, are hybridized to disappear along the $k_z$ direction, therefore not supporting the widely accepted Peierls-like electronic instability due to Fermi surface nesting. It is further demonstrated that applied hydrostatic pressure significantly reduces the interlayer spacing to destabilize the quasimolecular states, leading to a disappearance of the CDW phase at a pressure of ${sim}$2 GPa. The presently proposed underlying mechanism of the CDW order in CsV$_3$Sb$_5$ can also be applicable to other isostructural kagome lattices such as KV$_3$Sb$_5$ and RbV$_3$Sb$_5$.

Eliminating oscillation in partial sum approximation of periodic function

If we cannot obtain all terms of a series, or if we cannot sum up a series, we have to turn to the partial sum approximation which approximate a function by the first several terms of the series. However, the partial sum approximation often does not work well for periodic functions. In the partial sum approximation of a periodic function, there exists an incorrect oscillation which cannot be eliminated by keeping more terms, especially at the domain endpoints. A famous example is the Gibbs phenomenon in the Fourier expansion. In the paper, we suggest an approach for eliminating such oscillations in the partial sum approximation of periodic functions.

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most existing local descriptors which rely on a fragile local reference frame to gain rotation invariance, the proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. Meanwhile, the descriptor in YOHO also has a rotation equivariant part, which enables us to estimate the registration from just one correspondence hypothesis. Such property reduces the searching space for feasible transformations, thus greatly improves both the accuracy and the efficiency of YOHO. Extensive experiments show that YOHO achieves superior performances with much fewer needed RANSAC iterations on four widely-used datasets, the 3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. More details are shown in our project page: https://hpwang-whu.github.io/YOHO/.

Dynamic Sliding Window for Meeting Summarization

Recently abstractive spoken language summarization raises emerging research interest, and neural sequence-to-sequence approaches have brought significant performance improvement. However, summarizing long meeting transcripts remains challenging. Due to the large length of source contents and targeted summaries, neural models are prone to be distracted on the context, and produce summaries with degraded quality. Moreover, pre-trained language models with input length limitations cannot be readily applied to long sequences. In this work, we first analyze the linguistic characteristics of meeting transcripts on a representative corpus, and find that the sentences comprising the summary correlate with the meeting agenda. Based on this observation, we propose a dynamic sliding window strategy for meeting summarization. Experimental results show that performance benefit from the proposed method, and outputs obtain higher factual consistency than the base model.

Generation of robust spatiotemporal optical vortices with transverse orbital angular momentum beyond $10^2$

Recently, photons have been observed to possess transverse orbital angular momentum (OAM); however, it is unclear as whether they can hold a transverse OAM higher than 1. Here, we theoretically and experimentally demonstrate that high-order spatiotemporal Bessel optical vortices (STBOVs) can stably carry transverse OAM even beyond $10^2$. Through the inverse design of the spiral phase, an STBOV of any order can be controllably generated using a 4f pulse shaper. In contrast to conventional longitudinal OAM, the vector direction of the transverse OAM can be distinguished by the unique time-symmetrical evolution of STBOVs. More interestingly, the stability of STBOVs improves with their increasing orders owing to enhanced space-time coupling, making these beams particularly suitable for the generation of ultra-high transverse OAM. Our work paves the way for further research and application of this unique OAM of photons.

Improving 3D Object Detection with Channel-wise Transformer

Though 3D object detection from point clouds has achieved rapid progress in recent years, the lack of flexible and high-performance proposal refinement remains a great hurdle for existing state-of-the-art two-stage detectors. Previous works on refining 3D proposals have relied on human-designed components such as keypoints sampling, set abstraction and multi-scale feature fusion to produce powerful 3D object representations. Such methods, however, have limited ability to capture rich contextual dependencies among points. In this paper, we leverage the high-quality region proposal network and a Channel-wise Transformer architecture to constitute our two-stage 3D object detection framework (CT3D) with minimal hand-crafted design. The proposed CT3D simultaneously performs proposal-aware embedding and channel-wise context aggregation for the point features within each proposal. Specifically, CT3D uses proposals keypoints for spatial contextual modelling and learns attention propagation in the encoding module, mapping the proposal to point embeddings. Next, a new channel-wise decoding module enriches the query-key interaction via channel-wise re-weighting to effectively merge multi-level contexts, which contributes to more accurate object predictions. Extensive experiments demonstrate that our CT3D method has superior performance and excellent scalability. Remarkably, CT3D achieves the AP of 81.77% in the moderate car category on the KITTI test 3D detection benchmark, outperforms state-of-the-art 3D detectors.