## 1 Introduction  
Robotic manipulation has emerged as a critical area of research, both in industrial applications [1, 2, 3] and in everyday home environments [4, 5, 6], with significant attention focused on enabling robots to interact effectively with objects in their surroundings. A fundamental challenge in this domain is the need for comprehensive 3D information about objects, such as geometric shapes and structural details. Traditional methods in object manipulation, which rely on pre-existing 3D models, perform well with familiar objects but falter when faced with novel ones. Recent learning-based approaches show promise for simpler tasks with abundant training data but often struggle with more complex tasks, such as novel object assembly and rearrangement. In these cases, the ability to leverage 3D models becomes essential, underscoring the importance of active 3D reconstruction in enhancing robotic capabilities in diverse and unstructured environments. In 3D modeling, traditional methods based on sensors, such as LiDAR [7; 8] or structured light sensors [9; 10], have been used for 3D reconstruction of novel objects. These high-cost sensors capture detailed depth information and are capable of reconstructing fine geometry. However, these methods often necessitate changes in various settings and configurations during the reconstruction process, leading to inevitable human intervention and increased complexity. To overcome the limitations of these high-cost sensors, recent advancements have focused on using RGB cameras for 3D reconstruction. One such method is structure-from-motion (SfM), which also uses RGB images to reconstruct 3D models. However, SfM often requires multiple viewpoints and precise alignment to generate accurate geometry and can be computationally expensive for complex objects. More recently, neural implicit representations, such as neural radiance fields (NeRF) [11], have emerged as an alternative, enabling 3D reconstruction of unknown objects using only RGB images. NeRF has garnered significant attention in the robot vision field due to its ability to produce high-quality 3D reconstructions without requiring high-cost sensors. NeRF's scene-agnostic 3D reconstruction capabilities have facilitated a range of complex robotic downstream tasks [12; 13; 14; 15; 16]. Building on these advances, recent studies in active robotic 3D reconstruction have increasingly incorporated neural implicit representations [17; 18; 19; 20]. However, most research in this area assumes rooted objects, which prevents the reconstruction of hidden surfaces such as the bottom of an object, the back of a partially occluded object, or intricate details in cavities and recesses. This issue is particularly pronounced in single fixed-based robotic manipulator systems, where the reachable view space is limited to the manipulator's workspace, restricting object 3D reconstruction to these limited viewpoints [21; 22]. As a result, critical parts of the object, such as its bottom side or occluded regions, may not be reconstructed accurately, leading to incomplete models. To address these challenges, we propose a two-stage robotic active 3D reconstruction pipeline, named See-Then-Grasp (STG), using two cameras within a single robot manipulator system. In the first stage, a hand-eye camera captures the object's approximate geometry. In the second stage, the robot grasps the object and presents it to a fixed external camera, achieving full 3D coverage. To expedite viewpoint selection, we introduce a gradient-based pose optimization technique, enabling the robot to efficiently identify and observe uncertain regions, ensuring a complete 3D reconstruction. Our proposed pipeline is illustrated in Figure 1.  
Our contributions can be summarized as follows:  
* We propose a novel two-stage active robotic 3D reconstruction pipeline to address the limitations of fixed-base single manipulator systems, which are constrained to partial object reconstruction due to restricted view space.