BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects (2303.14158v1)

Abstract: We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object. Our method works for arbitrary rigid objects, even when visual texture is largely absent. The object is assumed to be segmented in the first frame only. No additional information is required, and no assumption is made about the interaction agent. Key to our method is a Neural Object Field that is learned concurrently with a pose graph optimization process in order to robustly accumulate information into a consistent 3D representation capturing both geometry and appearance. A dynamic pool of posed memory frames is automatically maintained to facilitate communication between these threads. Our approach handles challenging sequences with large pose changes, partial and full occlusion, untextured surfaces, and specular highlights. We show results on HO3D, YCBInEOAT, and BEHAVE datasets, demonstrating that our method significantly outperforms existing approaches. Project page: https://bundlesdf.github.io

• The paper presents a novel co-design that concurrently optimizes 6-DoF pose tracking and neural implicit 3D reconstruction for unknown objects.
• It integrates a hybrid SDF-based Neural Object Field with dynamic pose graph optimization to overcome occlusions and texture-less challenges.
• The approach achieves state-of-the-art results on benchmarks, notably 96.52% ADD-S on HO3D, demonstrating robust performance in dynamic scenes.

An Expert Review of "BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects"

The paper "BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects" presents an innovative method that tackles the dual challenge of six degrees of freedom (6-DoF) pose tracking and 3D reconstruction of unknown objects from monocular RGBD video sequences. This is achieved through a novel integration of concurrent processes: online pose graph optimization and Neural Object Field (NOF) learning, providing a substantial advancement in handling dynamic and complex scenes where object-specific models are not available.

Methodological Advancements

The methodology proposed by the authors stands out due to its unique integration of two parallel computational threads: pose graph optimization and neural implicit reconstruction. This co-design promises robustness against common challenges like occlusions, specularities, and texture-less surfaces.

1. Neural Object Field (NOF): The NOF is a pivotal component that captures both geometric and appearance details of the object concurrently with pose estimation. This field employs an SDF-based representation, augmented by a novel hybrid SDF approach, handling uncertainties in scene segmentation and providing a smoothly continuous surface representation.
2. Pose Graph Optimization: The paper introduces an effecient pose graph optimization algorithm that dynamically updates frame-to-frame correspondences leveraging feature points and reprojective associations within a memory-efficient framework. This informs both the NOF and overall pose consistency across frames.
3. Memory Pool Strategy: Efficient information retention methods are employed, such as a dynamic keyframe memory pool that preserves multi-view diversity. This is essential for maintaining accurate pose estimates over lengthy video sequences where appearance changes or occlusions may occur.

Numerical Results and Analysis

Across several datasets, including HO3D, YCBInEOAT, and BEHAVE, the proposed method demonstrates exemplary performance and achieves state-of-the-art results. The improvement is quantitatively evidenced by the high AUC percentages for both ADD and ADD-S metrics, indicating superior robustness in pose tracking and accuracy in 3D reconstruction:

• On the HO3D dataset, BundleSDF achieves an ADD-S of 96.52%, outperforming previous methods, notably when dealing with texture-less and partially occluded objects.
• On the YCBInEOAT dataset, the method maintains a competitive edge with an ADD-S of 93.77% and showcases robustness, particularly under varying object interactions with robotic manipulators.
• The BEHAVE dataset, known for its complexity due to dynamic human-object interactions, further cements BundleSDF's superior performance, achieving a 67.52% score in ADD.

These results signify the method's robustness against drastic environmental changes and emphasize the concurrent tracking and reconstruction strategy's effectiveness in mitigating tracking drift over time.

Practical and Theoretical Implications

Practically, the ability to perform real-time tracking and reconstruction from RGBD videos without pre-learned models or category-specific information opens new avenues in AR/VR applications, autonomous robotic systems, and real-time digital twins. The integration of NOFs could significantly enhance robotic perception, enabling autonomous systems to navigate and manipulate in unknown environments with greater reliability.

Theoretically, this work contributes to the ongoing conflation of neural representation learning with SLAM-like systems, demonstrating how these paradigms can reinforce one another for robust scene understanding. Future explorations might deepen these integrations or extend them to incorporate priors for deformable object scenarios, enriching the repository of challenging computer vision tasks amenable to neural implicit approaches.

Conclusions

In conclusion, "BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects" provides a comprehensive, well-supported method for addressing complex computer vision tasks. The blend of pose optimization with neural network-derived SDFs sets a precedent for future research seeking to enhance object tracking and scene reconstruction in dynamically evolving and unstructured environments. This paper is a significant contribution to the field, with promising implications for future AI and robotics advancements.