Multicamera 3D Reconstruction of Dynamic Surgical Cavities: NonRigid Registration and Point Classification Deformable objects and surfaces are ubiquitous in the daily lives of humans xe2x80x93 from the garments in fashion to soft tissues within the body. Because of this routine interaction with soft materials, humans are adept and trained in manipulation of deformable objects while avoiding irreversible damage. The dexterity and care involved is largely facilitated through a combination of the human haptic sense of touch and visual observations of object deformation 1. While this scenario presents itself as a trivially intuitive task, it becomes significantly more difficult and complex with the deprivation of both 3D depth perception and haptic senses. This deprived state is not dissimilar to the scenarios encountered in many robotassisted minimally invasive surgeries. As a result, unintentional tissue damage can occur due to lack of force feedback and fine 3D visibility 2. One approach to remediate these issues combines realtime dynamic 3D reconstruction and visionbased force estimation for haptic feedback. Toward that end, this work continues research in a series of studies focusing on multicamera 3D reconstruction of dynamic surgical cavities. Previous work introduced a novel approach of camera grouping and pair sequencing 3. This paper builds upon that work by introducing a method for nonrigid, sparse point cloud registration and subsequent point classification. In particular, to enable deformation and force analyses, surfaces are locally classified into three categories: static, shifting and deforming. The topics addressed in this paper present open challenges and ongoing research directions for researchers to this day 4, and provide a step towards realtime 3D reconstruction and force feedback in robotassisted surgery.

Transfer learning for visionbased tactile sensing Due to the complexity of modeling the elastic properties of materials, the use of machine learning algorithms is continuously increasing for tactile sensing applications. Recent advances in deep neural networks applied to computer vision make visionbased tactile sensors very appealing for their highresolution and low cost. A soft optical tactile sensor that is scalable to large surfaces with arbitrary shape is discussed in this paper. A supervised learning algorithm trains a model that is able to reconstruct the normal force distribution on the sensorxe2x80x99s surface, purely from the images recorded by an internal camera. In order to reduce the training times and the need for large datasets, a calibration procedure is proposed to transfer the acquired knowledge across multiple sensors while maintaining satisfactory performance.