CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation (2203.01929v1)

Abstract: This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation. In contrast to instance-level pose estimation, we focus on a more challenging problem where CAD models are not available at inference time. Existing approaches mainly follow a complex multi-stage pipeline which first localizes and detects each object instance in the image and then regresses to either their 3D meshes or 6D poses. These approaches suffer from high-computational cost and low performance in complex multi-object scenarios, where occlusions can be present. Hence, we present a simple one-stage approach to predict both the 3D shape and estimate the 6D pose and size jointly in a bounding-box free manner. In particular, our method treats object instances as spatial centers where each center denotes the complete shape of an object along with its 6D pose and size. Through this per-pixel representation, our approach can reconstruct in real-time (40 FPS) multiple novel object instances and predict their 6D pose and sizes in a single-forward pass. Through extensive experiments, we demonstrate that our approach significantly outperforms all shape completion and categorical 6D pose and size estimation baselines on multi-object ShapeNet and NOCS datasets respectively with a 12.6% absolute improvement in mAP for 6D pose for novel real-world object instances.

CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation

CenterSnap introduces a novel approach to address the challenges of multi-object 3D shape reconstruction and 6D pose and size estimation from a single-view RGB-D observation. This paper departs from traditional instance-level methods where CAD models are available during inference, focusing instead on category-level settings where novel object instances and their models are not known a priori. The authors present a significant advancement by formulating a one-stage, bounding-box proposal-free method that simultaneously reconstructs 3D shapes and estimates pose and size using object-centric features derived from spatial centers in the input image.

Traditional approaches rely heavily on complex multi-stage pipelines that first localize and detect each object, followed by regression to 3D meshes or 6D poses, often resulting in a high computational load and reduced performance, particularly under conditions of occlusion and in real-time applications. CenterSnap addresses these inefficiencies through a per-pixel representation strategy that treats object instances as spatial centers. Each center encodes the complete geometric and pose information of an object, facilitating rapid reconstruction and estimation in a single-forward pass.

Numerical evaluations reveal that CenterSnap significantly surpasses all existing benchmarks for shape completion, 6D pose, and size estimation on the ShapeNet and NOCS datasets. The approach boasts an absolute improvement of 12.6% in mAP for 6D pose estimation on real-world novel object instances, highlighting its efficacy across diverse and previously unseen scenarios.

The practical implications of CenterSnap's contributions lie in its potential applications to robotics and automation, where real-time feedback and scene understanding are essential. By streamlining detection and reconstruction into a single-shot pipeline, it enables faster decision-making and enhances the capabilities of machines in environments requiring dynamic interaction with multiple objects. Theoretically, it offers a scalable solution by integrating shape priors learned from extensive datasets, thereby providing robustness in handling intra-category variance.

Future developments in AI could build upon the foundation laid by this method, exploring enhanced feature extraction from RGB-D data and its integration with newer sensor technologies, potentially expanding applications beyond static setups to dynamic real-world environments. The adoption of center-based representations for other multi-dimensional recognition tasks in AI could further enhance the depth of understanding and efficiency of machine perception systems.

In summary, CenterSnap represents a pivotal shift in single-stage object recognition and reconstruction strategies, combining computational efficiency with robust performance. This paper is a valuable contribution to the field, offering insights and a promising pathway toward more efficient and real-time applications in multi-object scenarios.