μ-MAR: Multiplane 3D Marker based Registration for Depth-sensing Cameras (1708.01405v1)

Abstract: Many applications including object reconstruction, robot guidance, and scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown an it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the Signal-to-Noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, {\mu}-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The ...

• The paper introduces μ-MAR, a novel model-based 3D registration method specifically designed for depth-sensing cameras facing challenges like low signal-to-noise ratio and resolution.
• μ-MAR utilizes known multiplane markers to achieve more accurate coarse and fine registration of 3D point clouds, outperforming traditional methods like ICP in noisy environments.
• This robust approach enables more reliable object reconstruction and has practical applications in fields like robotics and consumer electronics where low-cost depth sensors are prevalent.

An Analysis of $\mu$-MAR: A Model-Based Registration Method for Depth-Sensing Cameras

The paper "$\mu$-MAR: Multiplane 3D Marker based Registration for Depth-sensing Cameras" presents a novel approach to the registration of 3D point clouds obtained from depth-sensing cameras. The method addresses the challenges posed by low Signal-to-Noise ratio (SNR) and resolution, which are typical disadvantages of such cameras. The primary objective of $\mu$-MAR is to improve both coarse and fine registration of sets of 3D points into a unified coordinate system, using a technique that employs multiplane registration markers.

Overview of the Approach

$\mu$-MAR leverages known 3D markers, typically composed of multiple planes like cubes or pyramids, placed around the object of interest. This approach emphasizes the use of model-based registration to mitigate the inaccuracies caused by noisy data. The cornerstone of the method is a multi-view registration strategy which iteratively aligns each view to the remainder, refining the registration as more views are added.

This method is established on several key steps: the detection and extraction of planar models from the segmented view, the registration of these models by aligning each view with the mean values of the others, and the subsequent transformation of the object of interest into the common coordinate system. It particularly shines in environments where traditional feature-based methods, such as RANSAC with SIFT features or ICP, fail due to insufficient texture or unreliable geometric data.

Experimental Validation

The authors validate $\mu$-MAR through extensive experimentation on synthetic and real datasets. In synthetic scenarios, the method demonstrates robustness against varying levels of Gaussian noise, outperforming traditional ICP methods in terms of registration accuracy. The quantitative assessments using Hausdorff distances underscore its superior performance, yielding significantly lower errors in comparison to ICP.

In real-world testing, $\mu$-MAR was evaluated against other established methods such as RGBDemo and KinectFusion. These results, evaluated through visual inspection, affirm the capability of $\mu$-MAR to handle complex scenarios where objects lack sufficient geometric variation or contrast necessary for feature-based methods.

Implications and Future Directions

The introduction of $\mu$-MAR represents a noteworthy advancement in model-based 3D registration techniques. Its effectiveness in scenarios with low SNR and resolution data paves the way for more reliable object reconstruction applications, particularly in fields reliant on low-cost depth sensors like consumer electronics and robotics.

Looking forward, further enhancements and adaptation of $\mu$-MAR could include its application to dynamic scenes and more diverse marker configurations to handle a broader set of scenarios. Moreover, exploration into augmenting the method with constraints based on expected object accuracy and complexity could refine its adaptability to real-world constraints.

In conclusion, this paper provides a substantial contribution to the domain of computer vision by demonstrating a robust method for registering noisy 3D data through multiplane markers. Its practical applications and potential for further development signify an important step toward resolving common challenges in depth-sensing image processing.