Point cloud-based 3D single object tracking (3D SOT) plays a pivotal role in applications such as autonomous driving and robotic vision. Despite recent progress, most existing approaches rely solely on current-frame features for target localization. This approach overlooks temporal information that is crucial for robust tracking under occlusion, appearance variations, and sparse point clouds. In addition, the effectiveness of 3D SOT largely depends on the quality of feature fusion between the target template and the search region. Traditional fusion strategies often suffer from limited interaction capacity and weak discriminative representation. To address these challenges, we propose DT-Tracker, which performs multi-layer bidirectional feature interaction and temporal cue propagation to improve tracking robustness and feature discrimination capability. Specifically, we introduce a Dual-Context Propagation Network that applies bidirectional cross-attention across multiple layers between the template and search region, enabling deep semantic alignment and progressive feature refinement. Furthermore, we design a Temporal Context Fusion module that adaptively incorporates temporal cues from historical fusion features into the current frame, effectively improving resilience to occlusion and appearance drift. Extensive experiments on the KITTI and nuScenes datasets demonstrate that DT-Tracker achieves competitive results compared to existing representative methods.
The spatio-temporal relationship between the pixels of a video carries critical information for low-level 4D perception tasks. A single model that reasons about it should be able to solve several such tasks well. Yet, most state-of-theart methods rely on architectures specialized for the task at hand. We present L4P, a feedforward, general-purpose architecture that solves low-level 4D perception tasks in a unified framework. LAP leverages a pre-trained ViT-based video encoder and combines it with per-task heads that are lightweight and therefore do not require extensive training. Despite its general and feedforward formulation, our method is competitive with existing specialized methods on both dense tasks, such as depth or optical flow estimation, and sparse tasks, such as 2D/3D tracking. Moreover, it solves all tasks at once in a time comparable to that of single-task methods.
Efficiently reconstructing 3D scenes from monocular video remains a core challenge in computer vision, vital for applications in virtual reality, robotics, and scene understanding. Recently, frame-by-frame progressive reconstruction without camera poses is commonly adopted, incurring high computational overhead and compounding errors when scaling to longer videos. To overcome these issues, we introduce VideoLifter, a novel video-to-3D pipeline that leverages a local-to-global strategy on a fragment basis, achieving both extreme efficiency and SOTA quality. Locally, VideoLifter leverages learnable 3D priors to register fragments, extracting essential information for subsequent 3D Gaussian initialization with enforced inter-fragment consistency and optimized efficiency. Globally, it employs a tree-based hierarchical merging method with key frame guidance for inter-fragment alignment, pairwise merging with Gaussian point pruning, and subsequent joint optimization to ensure global consistency while efficiently mitigating cumulative errors. This approach significantly accelerates the reconstruction process, reducing training time by over 82 % while achieving better visual quality than SOTA methods.
High-quality, real-time dynamic scene reconstruction and rendering are essential for immersive applications. While techniques like 3D Gaussian Splatting (3DGS) succeed in static scenes, dynamic monocular scenarios still suffer from deformation, surface noise, and inconsistent view-dependent effects due to insufficient geometric constraints and inadequate multi-view consistency enforcement for monocular input. To address these challenges, we present Normal-Regularized and Multi-View-Consistent Gaussian Splatting (NVC-GS), a novel approach combining geometry-aware normal regularization with diffusion-based multi-view consistency. Our method explicitly preserves geometric consistency in dynamic objects through tailored normal constraints, while leveraging diffusion-driven latent space regularization to ensure cross-view rendering consistency, particularly for complex materials such as reflective surfaces. Experimental results demonstrate that our approach effectively improves geometric accuracy and visual quality in dynamic scenes while maintaining real-time capabilities, outperforming existing methods in terms of deformation handling, surface noise reduction, and rendering of reflective materials.
Self-supervised learning has emerged as a promising approach for acquiring transferable 3D representations from unlabeled 3D point clouds. Unlike 2D images, which are widely accessible, acquiring 3D assets requires specialized expertise or professional 3D scanning equipment, making it difficult to scale and raising copyright concerns. To address these challenges, we propose learning 3D representations from procedural 3D programs that automatically generate 3D shapes using simple 3D primitives and augmentations. Remarkably, despite lacking semantic content, the 3D representations learned from the procedurally generated 3D shapes perform on par with state-of-the-art representations learned from semantically recognizable 3D models (e.g., airplanes) across various downstream 3D tasks, such as shape classification, part segmentation, masked point cloud completion, and both scene semantic and instance segmentation. We provide a detailed analysis on factors that make a good 3D procedural programs. Extensive experiments further suggest that current 3D self-supervised learning methods on point clouds do not rely on semantics of 3D shapes, shedding light on the nature of 3D representations learned.
