Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching. This work proposes BundleTrack, a general framework for 6D pose tracking of novel objects, which does not depend upon 3D models, either at the instance or category-level. It leverages the complementary attributes of recent advances in deep learning for segmentation and robust feature extraction, as well as memory-augmented pose graph optimization for spatiotemporal consistency. This enables long-term, low-drift tracking under various challenging scenarios, including significant occlusions and object motions. Comprehensive experiments given two public benchmarks demonstrate that the proposed approach significantly outperforms state-of-art, category-level 6D tracking or dynamic SLAM methods. When compared against state-of-art methods that rely on an object instance CAD model, comparable performance is achieved, despite the proposed method's reduced information requirements. An efficient implementation in CUDA provides a real-time performance of 10Hz for the entire framework. Code is available at: https://github.com/wenbowen123/BundleTrack
翻译:跟踪视频序列中天体的 6D 形状对于机器人操作很重要。 但是, 多数先前的努力, 通常都假设目标对象的 CAD 模型, 至少在类别级别上, 可以用于离线培训或在线模板匹配。 这项工作提议了 BundleTrack, 6D 的总框架是新物体的跟踪, 无论是在实例还是类别一级, 这不取决于 3D 模型。 它利用了在深度学习中最近进展的互补属性, 以进行分解和强力地段提取, 以及存储式图像优化, 以保持空间时的一致性。 这使得目标对象的 CADA 模型可以在各种具有挑战性的情景下进行长期、 低深度的跟踪, 包括重大的隐蔽和对象动议。 给出了两个公共基准的全面实验表明, 拟议的方法大大超越了艺术状态、 6D 级跟踪或动态的 SLMM 方法。 与依靠对象实例 CADD 模型的州级方法相比, 尽管拟议方法减少了信息要求, 也实现了可比较的绩效。 CUDA 有效在 CUDA 123/ holdwentruz 整个框架的实时运行: AM123/ swentrock/ sumlax.