WildFab: Multi-Axis 3D Printing from Models in the Wild
本文提出WildFab框架,通过结合神经无符号距离场与广义绕数场处理复杂几何模型,实现无需支撑材料的多轴3D打印。
本文提出WildFab框架,通过结合神经无符号距离场与广义绕数场处理复杂几何模型,实现无需支撑材料的多轴3D打印。
本文提出使用曲面电容纺织品作为形状传感器,通过多视图前馈重建模型聚合电容接近场数据以恢复物体形状。
为解决动态环境下SLAM系统精度下降问题,提出RoSe-SLAM方法,通过从单目视频中提取语义特征并结合时空运动掩模生成模块,实现准确的相机跟踪和高质量几何重建。
This work aims to effectively transfer the superior generation capabilities of Text-Image-to-Video (TI2V) models—exhibited when conditioned on high-quality initial frames or detailed textual prompts—to their underlying Text-to-Video (T2V) task. To this end, the authors propose a hybrid self-distillation framework in which a single model acts as both teacher (in TI2V mode) and student (in T2V mode). The approach leverages off-policy trajectory anchoring, local policy optimization, and velocity-level supervision signals to enable precise policy correction while avoiding condition-state mismatches. Notably, this method is the first to integrate privileged priors with online policy fine-tuning, significantly enhancing base T2V generation quality without requiring additional data, while also further improving TI2V performance—thereby comprehensively strengthening the model’s video synthesis capabilities.
This work addresses the challenge of trajectory optimization for long-range paths in redundant robotic multi-axis additive manufacturing under stringent deposition position constraints and time-varying collision constraints. The authors propose a collision-aware trajectory optimization framework that models the kinematic relationship between the nozzle and workpiece using relative Jacobians, captures dynamic geometric evolution through differentiable signed distance fields (SDFs), and enforces hard deposition position constraints via iterative projection onto the self-motion manifold. Optimization efficiency is enhanced by restricting gradient updates to the tangent space. Experimental results on an 8-degree-of-freedom platform demonstrate sub-10-micron average nozzle positioning error, a 77.6% reduction in peak joint jerk, complete avoidance of collisions and posture violations, up to a 10.2× speedup over an SQP baseline, and successful fabrication of complex unsupported structures.
本文提出WildFab框架,通过结合神经无符号距离场与广义绕数场处理复杂几何模型,实现无需支撑材料的多轴3D打印。
本文提出使用曲面电容纺织品作为形状传感器,通过多视图前馈重建模型聚合电容接近场数据以恢复物体形状。
为解决动态环境下SLAM系统精度下降问题,提出RoSe-SLAM方法,通过从单目视频中提取语义特征并结合时空运动掩模生成模块,实现准确的相机跟踪和高质量几何重建。
This work aims to effectively transfer the superior generation capabilities of Text-Image-to-Video (TI2V) models—exhibited when conditioned on high-quality initial frames or detailed textual prompts—to their underlying Text-to-Video (T2V) task. To this end, the authors propose a hybrid self-distillation framework in which a single model acts as both teacher (in TI2V mode) and student (in T2V mode). The approach leverages off-policy trajectory anchoring, local policy optimization, and velocity-level supervision signals to enable precise policy correction while avoiding condition-state mismatches. Notably, this method is the first to integrate privileged priors with online policy fine-tuning, significantly enhancing base T2V generation quality without requiring additional data, while also further improving TI2V performance—thereby comprehensively strengthening the model’s video synthesis capabilities.
This work addresses the challenge of trajectory optimization for long-range paths in redundant robotic multi-axis additive manufacturing under stringent deposition position constraints and time-varying collision constraints. The authors propose a collision-aware trajectory optimization framework that models the kinematic relationship between the nozzle and workpiece using relative Jacobians, captures dynamic geometric evolution through differentiable signed distance fields (SDFs), and enforces hard deposition position constraints via iterative projection onto the self-motion manifold. Optimization efficiency is enhanced by restricting gradient updates to the tangent space. Experimental results on an 8-degree-of-freedom platform demonstrate sub-10-micron average nozzle positioning error, a 77.6% reduction in peak joint jerk, complete avoidance of collisions and posture violations, up to a 10.2× speedup over an SQP baseline, and successful fabrication of complex unsupported structures.