Improved point clouds from a heritage artifact depth low-cost acquisition

Abstract

Poor data acquisition from low-cost cameras, such as low-resolution depth maps or high level of noise or point clouds generated with insufficient information from an object, limits the use of such cameras for heritage artifacts 3D reconstruction. This work proposes to improve this depth low-cost acquisition by using a new approach based on the Super-Resolution technique. The proposed approach has been applied to several artifacts of the Federal University of Bahia Museum of Archaeology and Ethnology (MAE/UFBA). As shown in the results, our approach improved the quality of point clouds generated from tested heritage artifacts. Results indicate that whenever artifact geometry is gained via our method there is actual reconstruction of detail or accuracy improvements, whereas a reduction in number of points of the clouds, if any, would indicate the removal of inconsistencies or noise from the input data without loss of detail.

Improved low-cost 3D reconstruction pipeline by merging data from different color and depth cameras

“…For the captures using a low-cost depth sensor, we established the following acquisition procedure: take several depth captures, moving the sensor around the object, and defining the limits of the capture volume. The number of views captured is less than that of real-time approaches due to the additional processing required to ensure the quality of each capture (Raimundo and Apaza-Agüero, 2020). Considering the quality requirements for this proposed work, an interactive tool (Raimundo, 2018) is used to acquire the raw data from the depth sensor (Fig.…”

“…The acquisition step of a 3D reconstruction pipeline refers to the use of devices to capture data from objects in a scene, such as their geometry and color (Raimundo and Apaza-Agüero, 2020). One result of 3D geometry capture is the production of discrete points collection that demonstrates the model shape.…”

“…The scanner sensor has the advantages of fast measuring speed, robustness regarding external factors, and ease of acquiring information. Active sensors also have good performance in reconstructing texture-less and featureless surfaces (Chen et al, 2019, Raimundo andApaza-Agüero, 2020). The sensors need to be sensitive to small variations in the information acquired, since, for small differences in distance, the variation in the time it takes to reach two different points is very low, requiring low equipment latency and good response time.…”

“…The KinectFusion system (Newcombe et al, 2011) tracks the pose of portable depth cameras (Kinect) as they move through space and perform good threedimensional surface reconstructions in real-time. The Kinect sensor has considerable limitations, including temporal inconsistency and the low resolution of the captured color and depth images (Raimundo and Apaza-Agüero, 2020). This approach does not include the texturing step.…”

“…Silva et al (2013) provides a guided reconstruction process using Super-Resolution (SR) techniques, helping to increase the quality of the low-resolution data captured with a low-cost depth sensor. The method of data acquisition using low-cost depth cameras and SR is also improved by Raimundo and Apaza-Agüero (2020). Even with depth image improvements, a poor registration of captures can affect the final model's shape.…”