15 accepted papers
Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity. Current solvers are often limited to static predictions or require computationally intensive heuristic searches. We present CryoACE, an end-to-end f…
3DGS has shown outstanding performance in multi-view geometry, driving its adoption in visual SLAM. However, real-time semantic 3DGS mapping faces challenges. Current methods typically treat semantics as external priors, making it hard to integrate them into SLAM tracking or loop closure correction.…
Tensegrity robots combine rigid rods and elastic cables, offering high resilience and deployability but at the same time posing major challenges for locomotion control due to their underactuated and highly coupled dynamics. This letter introduces a morphology-aware reinforcement learning framework t
Designing a whole-body controller for loco-manipulation in unstructured real-world environments remains a formidable challenge. Previous approaches have primarily focused on extending the workspace of robotic arms while maintaining quadrupedal landing postures. However, these methods fail to fully e
SLAM systems based on NeRF have demonstrated superior performance in rendering quality and scene reconstruction for static environments compared to traditional dense SLAM. However, they encounter tracking drift and mapping errors in real-world scenarios with dynamic interferences. To address these i
Reconstruction under adverse rainy conditions poses significant challenges due to reduced visibility and the distortion of visual perception. These conditions can severely impair the quality of geometric maps, which is essential for applications ranging from autonomous planning to environmental moni…
Current Simultaneous Localization and Mapping (SLAM) methods based on Neural Radiance Fields (NeRF) or 3D Gaussian Splatting excel in reconstructing static 3D scenes but struggle with tracking and reconstruction in dynamic environments, such as real-world scenes with moving elements. Existing NeRF-b
Recent advancements in 3DGS-based explicit mapping have significantly improved SLAM performance, achieving more realistic environment reconstruction and faster processing. However, issues such as data loss caused by unstable data transmission, textureless and repetitive-texture often occur in real-w
D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization. However, the original 3D Gaussian Splatting lacks color representation for inputs in lowlight environments. Simply using enhanced images as inputs woul
We present MPDG-SLAM, a novel 3D Gaussian point cloud rendering SLAM method based on Motion Probability (MP) for dynamic interference handling. Current 3DGSSLAM approaches for dynamic environments often rely on optical flow estimation masks. However, these deep learning-based optical flow models are
Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints. However, existing monocular SLAM systems lack accurate depth estimation, which limits the accuracy of tracking and mapping performance. To address this limitation, we propose Mo
Realistic animatable human avatars from monocular videos are crucial for advancing human-robot interaction and enhancing immersive virtual experiences. While recent research on 3DGS-based human avatars has made progress, it still struggles with accurately representing detailed features of non-rigid
"We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. It incorporates appearance, geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems in high-quality rendering, scene understan…
An accurate and generalizable dense 3D reconstruction system has attracted much attention. However, existing 3D dense reconstruction systems are constrained by pre-training, and there is a need for enhanced reconstruction of texture and shape details. We propose an end-to-end 3D reconstruction syste