Fan Lu

Current structure-based molecular generation faces a fundamental dilemma: While static ligand modeling dominates computational approaches, real-world molecular interactions are inherently dynamic. Inspired by the conformational changes ligands undergo during semi-flexible docking, we propose a reinforcement learning (RL)–steered diffusion framework for semi-flexible molecular generation in protein pockets. By defining the denoising process as a Markov decision process, RL dynamically adjusts molecular structures through iterative exploration. Simultaneously, we incorporate multiple molecular properties as conditions to constrain the denoising policy to drug-like regions and perform self-supervised rigid training on both target-free and target-specific molecules. In addition, we propose a fast sampling strategy that accelerates sampling by 20 times, thereby improving the efficiency of training and sampling. Experiments demonstrate that our method outperforms state-of-the-art methods with a Vina score of −7.23 kcal/mol and an 11.53% success rate. Targeting unseen real-world proteins, the generated molecules preserve canonical interaction patterns while discovering previously unknown binding chemotypes.

LiDAR point cloud registration is a fundamental problem in 3D computer vision. Recent learning-based methods have significantly improved the robustness and accuracy of LiDAR point cloud registration, while most of these methods are designed for global registration. In this paper, we focus on the post-refinement problem of LiDAR point cloud registration, which has been largely overlooked in previous learning-based approaches. To address the correspondence error between LiDAR point clouds, we formulate the transformation refinement problem as a correspondence optimization problem and propose DCR, a diffusion-based correspondence refinement model. DCR is built upon denoising diffusion models and recovers accurate correspondences from noisy initial correspondences estimated by arbitrary global registration methods. To achieve precise correspondence refinement, we employ a residual-based modeling scheme to constrain the output distribution and design a conditional generation paradigm to control the randomness of diffusion models. We combine DCR with both learning-based and traditional global registration methods and perform experiments on three large-scale outdoor LiDAR point cloud datasets to verify the performance. Extensive experimental results demonstrate DCR’s versatility and effectiveness in improving the accuracy of LiDAR point cloud registration.

Collaborative perception seeks to mitigate the limitations of single-vehicle perception, such as occlusions, by facilitating communication and information sharing among connected vehicles. However, most existing works assume a homogeneous scenario where all vehicles share identity sensor types and perception model architectures. In contrast, real-world systems often involve heterogeneous agents with diverse sensor configurations and independently developed models. In such settings, directly exchanging features without proper alignment can significantly degrade performance and hinder effective collaboration. While some methods have been proposed to address heterogeneity, they typically require retraining or access to internal model parameters, making them impractical for scalable deployment. To address these challenges, we propose DiffAlign, a plug-and-play adapter that enables feature alignment across heterogeneous agents in a training-free and model-agnostic manner. DiffAlign treats received BEV features as noisy latent representations and progressively refines them through a pretrained diffusion process. This alignment strategy does not require access to model internals or any retraining, which makes it both scalable and privacy-preserving while supporting diverse sensor modalities and perception backbones. Extensive experiments on simulated OPV2V and real-world V2V4Real datasets demonstrate that DiffAlign consistently improves detection performance in heterogeneous settings, improving CoBEVT by 132.01% and 91.95%, respectively. Our method provides a practical path toward scalable, generalizable, and deployment-ready collaborative perception.

Multi-agent collaborative perception mitigates the issues of limited perception range and occlusion in single-vehicle perception through shared sensing information, resulting in improved perception performance. However, current collaborative strategies often fail to differentiate moving objects from the background in complex scenarios. This leads to critical object data being obscured by excessive background information during data fusion, complicating effective integration. Additionally, efficiently leveraging historical perception data remains a significant challenge. To address these issues, we propose a parameter-free motion attribute extraction module that captures motion features between consecutive frames, enabling more efficient utilization of historical information to enhance current perception. Specifically, we design two temporal modules to comprehensively explore both ego and collaborators’ historical information. Furthermore, we propose a method that simultaneously considers the relationships between global appearance features and local motion features of shared information, thereby enabling better integration of foreground object data during collaboration. We validate our algorithm through experiments conducted on both simulated and real-world collaborative benchmarks. Both qualitative and quantitative results demonstrate that our proposed MHCPP significantly outperforms state-of-the-art (SOTA) methods in terms of 3D object detection and prediction tasks.

Recently, general-purpose features for event camera data have become increasingly important in advancing event-based vision applications. Current methods typically adopt pre-training paradigms, yielding promising performance. However, the limited data and sparse spatial information of events hinder effective use of pretraining for rich semantic learning. In this paper, we tackle semantic scarcity by transferring knowledge from large pre-trained image models, without increasing event training data. Concretely, we propose a novel image-to-event knowledge distillation method named I2EKD. Acknowledging that different backbones suit different applications, we fix the teacher and keep the student architecture flexible. To improve versatility, we equip I2EKD with two model-agnostic objectives at the logit and feature levels. Additionally, without task-specific objectives or labels, I2EKD avoids re-distillation and transfers well to downstream applications. Furthermore, leveraging DINOv2 as the teacher, whose feature distribution is built from billions of data, the student can swiftly mimic the superior distribution in a data-efficient manner. Compared with the SOTA pre-training method, I2EKD generates outperforming or comparable features with 1/15 training cost (1/10 data × 2/3 epochs). Extensive experiments on different vision tasks (object recognition, semantic segmentation, and monocular depth) verify the effectiveness of our method. Notably, I2EKD achieves top-1 object recognition accuracy of 70.72%, leading the pre-training SOTA by 5.89%.

Although recent efforts have extended Neural Radiance Fields (NeRF) into LiDAR point cloud synthesis, the majority of existing works exhibit a strong dependence on precomputed poses. However, point cloud registration methods struggle to achieve precise global pose estimation, whereas previous pose-free NeRFs overlook geometric consistency in global reconstruction. In light of this, we explore the geometric insights of point clouds, which provide explicit registration priors for reconstruction. Based on this, we propose Geometry guided Neural LiDAR Fields(GeoNLF), a hybrid framework performing alternately global neural reconstruction and pure geometric pose optimization. Furthermore, NeRFs tend to overfit individual frames and easily get stuck in local minima under sparse-view inputs. To tackle this issue, we develop a selective-reweighting strategy and introduce geometric constraints for robust optimization. Extensive experiments on NuScenes and KITTI-360 datasets demonstrate the superiority of GeoNLF in both novel view synthesis and multi-view registration of low-frequency large-scale point clouds.

Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS), LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple shift from image NVS methods while ignoring the dynamic nature and the large-scale reconstruction problem of LiDAR point clouds. In light of this, we propose LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis. In consideration of the sparsity and large-scale characteristics, we design a 4D hybrid representation combined with multi-planar and grid features to achieve effective reconstruction in a coarse-to-fine manner. Furthermore, we introduce geometric constraints derived from point clouds to improve temporal consistency. For the realistic synthesis of LiDAR point clouds, we incorporate the global optimization of ray-drop probability to preserve cross-region patterns. Extensive experiments on KITTI-360 and NuScenes datasets demonstrate the superiority of our method in accomplishing geometry-aware and time-consistent dynamic reconstruction. Codes are available at https://github.com/ispc-lab/LiDAR4D.

Outdoor LiDAR point clouds are typically large-scale and complexly distributed. To achieve efficient and accurate registration, emphasizing the similarity among local regions and prioritizing global local-to-local matching is of utmost importance, subsequent to which accuracy can be enhanced through cost-effective fine registration. In this paper, a novel hierarchical neural network with double attention named HDMNet is proposed for large-scale outdoor LiDAR point cloud registration. Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to patch manner, which significantly improves the registration performance. Moreover, in order to further utilize the sparse matching information from deeper layer, we develop a novel trainable embedding mask to incorporate the confidence scores of correspondences obtained from pose estimation of deeper layer, eliminating additional computations. The high-confidence keypoints in the sparser point cloud of the deeper layer correspond to a high-confidence spatial neighborhood region in shallower layer, which will receive more attention, while the features of non-key regions will be masked. Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HDMNet.

Point cloud registration is a fundamental problem in 3D computer vision. Outdoor LiDAR point clouds are typically large-scale and complexly distributed, which makes the registration challenging. In this paper, we propose an efficient hierarchical network named HRegNet for large-scale outdoor LiDAR point cloud registration. Instead of using all points in the point clouds, HRegNet performs registration on hierarchically extracted keypoints and descriptors. The overall framework combines the reliable features in deeper layer and the precise position information in shallower layers to achieve robust and precise registration. We present a correspondence network to generate correct and accurate keypoints correspondences. Moreover, bilateral consensus and neighborhood consensus are introduced for keypoints matching, and novel similarity features are designed to incorporate them into the correspondence network, which significantly improves the registration performance. In addition, we design a consistency propagation strategy to effectively incorporate spatial consistency into the registration pipeline. The whole network is also highly efficient since only a small number of keypoints are used for registration. Extensive experiments are conducted on three large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet. The source code of the proposed HRegNet is available at https://github.com/ispc-lab/HRegNet2 .