19 accepted papers
3D visual grounding encompassing 3D referring expression comprehension (3DREC) and segmentation (3DRES) requires robust cross-modal representation to achieve fine-grained semantic alignment and precise geometric reasoning. However, most methods employ unimodal pre-trained encoders that transfer visu…
Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medical image datasets suffer from acquisition noise and annotation ambiguity, causing pervasive data uncertainty that substantially undermines model rob
Semantic understanding of large-scale aerial scenes represents a critical challenge in 3D computer vision, hindered by the prohibitive cost of dense annotation. This paper introduces EvoPropGS, a novel approach for the semantic segmentation of 3D Gaussian Splatting models that requires only minimal
We introduce the Hybrid Vector-Occupancy Field (HVOF), a new implicit 3D representation for reconstructing both open and closed surfaces from sparse point clouds. Existing approaches, such as occupancy field and signed distance fields, face severe limitations. They struggle with open surfaces, while
Outlier rejection for correspondence-based point cloud registration confronts two fundamental challenges in real-world scenarios. First, low-overlap regions yield sparse and fragmented inlier distributions that are difficult to discover using conventional one-step global search strategies. Second, l
Referring expression comprehension and segmentation (RECS) task plays a vital role in remote sensing due to its high efficiency in multi-tasking. However, RECS has reached a performance bottleneck rooted in representational insufficiency, primarily due to cross-task representational fragmentation in
With the growing accessibility of large-scale 3D point clouds from LiDAR and photogrammetric techniques, 3D change detection (3DCD) has become essential for understanding dynamic scenes. Existing methods typically formulate this as segmentation, treating each point independently for binary classific
Text-driven 3D editing, enabled by advancements in 3D reconstruction techniques such as NeRF and 3D Gaussian Splatting, aims to provide intuitive scene customization. However, existing methods frequently exhibit limitations in controllability and consistency. To address these shortcomings, we propos
Human pose generation is a complex task due to the non-rigid and highly variable nature of human body structures and appearances. However, existing methods often overlook the fundamental differences between spatial transformations of poses and texture generation for appearance, which makes them pron…
Domain generalization in 3D segmentation is a critical challenge in deploying models to unseen environments. Current methods mitigate the domain shift by augmenting the data distribution of point clouds. However, the model learns global geometric patterns in point clouds while ignoring the category-…
Vision-language models like CLIP have demonstrated strong zero-shot generalization, making them valuable for various downstream tasks through prompt learning. However, existing test-time prompt tuning methods, such as entropy minimization, treat both text and visual prompts as fixed learnable parame…
With its powerful visual-language alignment capability, CLIP performs well in zero-shot and few-shot learning tasks. However, we found in experiments that CLIP's logits suffer from serious inter-class confusion problems in downstream tasks, and the ambiguity between categories seriously affects the…
3D Change Detection (3DCD) has gradually become another research hotspot after image change detection. Recent works focus on using artificial labels for supervised or weakly-supervised training of siamese networks to segment changed points. However, labeling every points of multi-temporal point clou…
Learning representations from numerous 2D image data has shown promising performance, yet very few works apply this representations to point cloud registration. In this paper, we explore how to leverage the 2D information to assist the point cloud registration, and propose IAPReg, an Image-Assisted…
Point cloud registration is a fundamental task in 3D computer vision. Recent advances have shown that graph-based methods are effective for outlier rejection in this context. However, existing clique-based methods impose overly strict constraints and are NP-hard, making it difficult to achieve both…
We propose a transformation diffusion model for point cloud registration to balance precision and efficiency. Our method formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation, which is represented by quaternion and translation. Specif…
Inliers estimation constitutes a pivotal step in partially overlapping point cloud registration. Existing methods broadly obey coordinate-based scheme where inlier confidence is scored through simply capturing coordinate differences in the context. However this scheme results in massive inlier misin…
3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving. Sparse and occluded data in scene point clouds introduce variations in the appearance of tracked objects, adding complexity to the task. In this research, we unveil…
Multi-instance point cloud registration is the problem of estimating multiple rigid transformations between two point clouds. Existing solutions rely on global spatial consistency of ambiguity and the time-consuming clustering of highdimensional correspondence features, making it difficult to handle…