6 DoF (“degrees of freedom”) position tracking from monocular RGBD video and 3D reconstruction of unknown objects are two fundamental (and closely related) problems in computer vision. Solving these problems will enable applications in various fields such as augmented reality, robot manipulation, learning from demonstrations, and the transition from simulation to reality. Previous solutions often treated these two issues separately. For example, neural scene representations have successfully produced realistic 3D object models.
However, these methods rely on real-world item masks and established camera positions. Perfect 3D reconstruction is also prevented if a constantly moving camera captures a stationary object (for example, Figure 1 below: When placed on a table, the bottom of the object is never visible). On the other hand, textured 3D models of test items are often required in advance for pre-training and online template matching, such as his instance-level 6-DoF object localization and tracking algorithms. Category-level procedures can be generalized to new object instances that fall into the same category. Still, there are problems with undistributed cases and categories of objects that have not yet been seen.
They propose to circumvent these limitations by combining solutions to these two problems in this study. Their method is conceptually similar to his previous work in SLAM at the object level. Their approach works on the assumption that in the first frame of the video he needs a 2D object mask and the items are fixed. Except for these two conditions, the object is free to move around during the video. Still, they relax a number of assumptions, allowing them to deal with occlusion, specularity, lack of visual texture and geometric cues, and sudden object motion. A memory pool, an online pose graph optimization mechanism, and a simultaneous neural object field for reconstructing 3D form and appearance are key components of their approach. Figure 1 shows the resilience of their approach.
NVIDIA researchers have proposed a new approach to 3-D reconstruction from monocular RGBD videos using 6-DoF object tracking. When using their technique, you need to segment the object in the first frame. Their technique utilizes two concurrent threads, each running an online graph pose optimization and a neural object field representation, to solve difficult problems such as quick motion, partial and full occlusion, lack of textures, and specular highlights. I can handle the situation. They show state-of-the-art results on several datasets compared to traditional techniques. Future research will focus on using pre-shapes to recreate hidden components.
Below is a summary of their contributions.
• An entirely new technique for 3D reconstruction of the original unidentified dynamic object and causal 6-DoF pose tracking.
• Introduces a hybrid SDF representation to deal with uncertain free space caused by certain challenges that arise in dynamic object-centric settings, such as noisy segmentation and external occlusion due to interactions.
• Experiments on three public benchmarks demonstrate state-of-the-art performance over existing approaches.
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Aneesh Tickoo is a consulting intern at MarktechPost. He is currently pursuing a Bachelor’s Degree in Data Science and Artificial Intelligence from the Indian Institute of Technology (IIT), Bhilai. He spends most of his time on projects aimed at harnessing the power of machine learning. His research interest is image processing and his passion is building solutions around it. He loves connecting with people and collaborating on interesting projects.