3D scene flow represents the dense per-point motion field in dynamic scenes, playing a crucial role in various downstream tasks, including motion segmentation, dynamic scene reconstruction, 4D content generation, etc. However, previous regression-based works commonly suffer from unreliable correlations caused by locally constrained search ranges and struggle with the absence of timely feedback regarding the flow estimation uncertainty during training. To address these challenges, we propose a novel uncertainty-aware network for scene flow estimation, termed DifFlow3D, based on the conditional probabilistic diffusion model. Hierarchical diffusion-based flow estimation blocks are designed to enhance the correlation robustness and resilience to challenging cases, e.g., dynamics, noisy inputs, repetitive patterns, etc. To mitigate the generation diversity, three key flow-related features are leveraged as conditions in our diffusion model. Furthermore, we develop an uncertainty estimation module within diffusion to assess the reliability of estimated scene flow dynamically. A Hidden State Denoising strategy (HSD) is also introduced to further boost the stability of the reverse denoising process. Extensive experiments conducted on four scene flow datasets, including both synthetic and real-world datasets (FlyingThings3D, KITTI 2015, Argoverse, and Waymo Open), demonstrate the superiority of our proposed DifFlow3D. Compared to prior state-of-the-art methods, DifFlow3D has 26.0%, 36.4%, 35.3%, and 17.7% EPE3D reduction respectively across four datasets. Only trained on the synthetic FlyingThings3D dataset, our method achieves an unprecedented millimeter-level accuracy (0.0070 m EPE3D) on the real-scene KITTI dataset, highlighting its exceptional generalization capability. Additionally, our diffusion-based refinement paradigm can be seamlessly integrated as a plug-and-play module into existing scene flow networks, significantly enhancing their estimation accuracy. We also introduce our pre-trained scene flow estimator as explicit motion priors into the novel dynamic LiDAR view synthesis task, which validates its great potential for improving the 4D LiDAR reconstruction performance.

Point cloud videos can faithfully capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing world. However, designing an effective 4D backbone remains challenging, mainly due to the irregular and unordered distribution of points and temporal inconsistencies across frames. Also, recent transformer-based 4D backbones commonly suffer from large computational costs due to their quadratic complexity, particularly for long video sequences. To address these challenges, we propose a novel point cloud video understanding backbone purely based on the State Space Models (SSMs). Specifically, we first disentangle space and time in 4D video sequences and then establish the spatio-temporal correlation with the unified spatial-temporal Mamba blocks. The Intra-frame Spatial Mamba module is developed to encode locally similar geometric structures within a certain temporal stride. Subsequently, locally correlated tokens are delivered to the Inter-frame Temporal Mamba module, which integrates long-term point features across the entire video with linear complexity. Our proposed Mamba4D achieves competitive performance on the MSR-Action3D action recognition (+10.4% accuracy), HOI4D action segmentation (+0.7 F1 Score), and Synthia4D semantic segmentation (+0.19 mIoU) datasets. Mamba4D also has a significant efficiency improvement, especially for long video sequences, with 87.5% GPU memory reduction and × 5.36 speed-up. Codes are released at https://github.com/IRMVLab/Mamba4D.

3D scene flow represents the 3D motion of each point in the point cloud, which is a base 3D perception task for autonomous driving, like optical flow for 2D images. As non-learning methods are often inefficient or struggled to learn accurate correspondence in complex 3D real world, recent works turn to supervised learning methods, which require ground truth labels. However, acquiring the ground truth of 3D scene flow is challenging mainly due to the lack of sensors capable of capturing point-level motion and the complexity of accurately tracking each point in real-world environments. Therefore, it is important to resort to self-supervised methods, which do not require ground truth labels. In this paper, a novel unsupervised learning method of scene flow with LiDAR odometry is proposed, which enables the scene flow network can be trained directly on real-world LiDAR data without scene flow labels. In this structure, supervised odometry provides a more accurate shared cost volume for the interframe association of 3D scene flow. In addition, because static and occluded points are more suitable for using the pose transform while dynamic and non-occluded points are more suitable for using the scene flow transform, a static mask and an occlusion mask are designed to classify the states of points and a mask-weighted warp layer is proposed to transform source points in a divide-and-conquer manner. The experiments demonstrate that the divide-and-conquer strategy makes the predicted scene flow more accurate. The experiment results compared to other methods also show the application ability of our proposed method to real-world data. Our source codes are released at:https://github.com/IRMVLab/PSFNet.

Learning 3D scene flow from LiDAR point clouds presents significant difficulties, including poor generalization from synthetic datasets to real scenes, scarcity of real-world 3D labels, and poor performance on real sparse Li-DAR point clouds. We present a novel approach from the perspective of auto-labelling, aiming to generate a large number of 3D scene flow pseudo labels for real-world Li-DAR point clouds. Specifically, we employ the assumption of rigid body motion to simulate potential object-level rigid movements in autonomous driving scenarios. By updating different motion attributes for multiple anchor boxes, the rigid motion decomposition is obtained for the whole scene. Furthermore, we developed a novel 3D scene flow data augmentation method for global and local motion. By perfectly synthesizing target point clouds based on augmented motion parameters, we easily obtain lots of 3D scene flow labels in point clouds highly consistent with real scenarios. On multiple real-world datasets including LiDAR KITTI, nuScenes, and Argoverse, our method outperforms all previous supervised and unsupervised methods without requiring manual labelling. Impressively, our method achieves a tenfold reduction in EPE3D metric on the LiDAR KITTI dataset, reducing it from 0.190m to a mere 0.008m error.

Scene flow estimation, which aims to predict per-point 3D displacements of dynamic scenes, is a fundamen-tal task in the computer vision field. However, previ-ous works commonly suffer from unreliable correlation caused by locally constrained searching ranges, and struggle with accumulated inaccuracy arising from the coarse-to-fine structure. To alleviate these problems, we propose a novel uncertainty-aware scene flow estimation network(DifFlow3D) with the diffusion probabilistic model. Iter-ative diffusion-based refinement is designed to enhance the correlation robustness and resilience to challenging cases, e.g. dynamics, noisy inputs, repetitive patterns, etc. To re-strain the generation diversity, three key flow-related features are leveraged as conditions in our diffusion model. Furthermore, we also develop an uncertainty estimation module within diffusion to evaluate the reliability of esti-mated scene flow. Our DifFlow3D achieves state-of-the-art performance, with 24.0% and 29.1% EPE3D reduction respectively on FlyingThings3D and KITTI 2015 datasets. Notably, our method achieves an unprecedented millimeter-level accuracy (O.0078m in EPE3D) on the KITTI dataset. Additionally, our diffusion-based refinement paradigm can be readily integrated as a plug-and-play module into ex-isting scene flow networks, significantly increasing their estimation accuracy. Codes are released at https:// github.com/IRMVLab/DifFlow3D.

Point Cloud Interpolation confronts challenges from point sparsity, complex spatiotemporal dynamics, and the difficulty of deriving complete 3D point clouds from sparse temporal information. This paper presents NeuroGauss4D-PCI, which excels at modeling complex non-rigid deformations across varied dynamic scenes. The method begins with an iterative Gaussian cloud soft clustering module, offering structured temporal point cloud representations. The proposed temporal radial basis function Gaussian residual utilizes Gaussian parameter interpolation over time, enabling smooth parameter transitions and capturing temporal residuals of Gaussian distributions. Additionally, a 4D Gaussian deformation field tracks the evolution of these parameters, creating continuous spatiotemporal deformation fields. A 4D neural field transforms low-dimensional spatiotemporal coordinates ($x,y,z,t$) into a high-dimensional latent space. Finally, we adaptively and efficiently fuse the latent features from neural fields and the geometric features from Gaussian deformation fields. NeuroGauss4D-PCI outperforms existing methods in point cloud frame interpolation, delivering leading performance on both object-level (DHB) and large-scale autonomous driving datasets (NL-Drive), with scalability to auto-labeling and point cloud densification tasks.

Recently, transformer architecture has gained great success in the computer vision community, such as image classification, object detection, etc. Nonetheless, its application for 3D vision remains to be explored, given that point cloud is inherently sparse, irregular, and unordered. Furthermore, existing point transformer frameworks usually feed raw point cloud of N×3 dimension into transformers, which limits the point processing scale because of their quadratic computational costs to the input size N. In this paper, we rethink the structure of point transformer. Instead of directly applying transformer to points, our network (TransLO) can process tens of thousands of points simultaneously by projecting points onto a 2D surface and then feeding them into a local transformer with linear complexity. Specifically, it is mainly composed of two components: Window-based Masked transformer with Self Attention (WMSA) to capture long-range dependencies; Masked Cross-Frame Attention (MCFA) to associate two frames and predict pose estimation. To deal with the sparsity issue of point cloud, we propose a binary mask to remove invalid and dynamic points. To our knowledge, this is the first transformer-based LiDAR odometry network. The experiment results on the KITTI odometry dataset show that our average rotation and translation RMSE achieves 0.500°/100m and 0.993% respectively. The performance of our network surpasses all recent learning-based methods and even outperforms LOAM on most evaluation sequences.Codes will be released on https://github.com/IRMVLab/TransLO.

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves competitive performance in terms of both accuracy and efficiency. Codes are available at https://github.com/IRMVLab/RegFormer.

3-D scene flow characterizes how the points at the current time flow to the next time in the 3-D Euclidean space, which possesses the capacity to infer autonomously the nonrigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3-D scene flow by estimating optical flow and disparity separately. Learning 3-D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this article, the generated dense depth map is utilized to obtain explicit 3-D coordinates, which achieves direct learning of 3-D scene flow from 2-D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2-D pixels into the 3-D space. Outliers in the generated 3-D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3-D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3-D scene flow. The proposed method of self-supervised learning of 3-D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation.