Image segmentation has come a long way in the last decade, thanks to the advancement of neural networks. It is now possible to segment multiple objects in complex scenes in a matter of milliseconds, and the results are quite accurate. On the other hand, we have another task on our hands for 3D, instance segmentation, and we have a ways to go until we reach 2D image segmentation performance.
3D instance segmentation has become a critical task with significant applications in fields such as robotics and augmented reality. The goal of 3D instance segmentation is to predict object instance masks and their corresponding categories in a 3D scene. While notable progress has been made in this field, existing methods predominantly operate under a closed set paradigm, where the set of object categories is limited and closely related to the data sets used for training.
This limitation raises two fundamental problems. First, closed vocabulary approaches struggle to understand scenes beyond the categories of objects encountered during training, leading to potential difficulties in recognizing new objects or misclassifying them. Second, these methods are inherently limited in their ability to handle free-form queries, which prevents their effectiveness in scenarios that require understanding and acting on specific object descriptions or properties.
Open vocabulary approaches are proposed to address these challenges. These approaches can handle free-form queries and allow zero-shot learning of object categories that are not present in the training data. By taking a more flexible and expansive approach, open vocabulary methods offer several advantages in tasks such as scene comprehension, robotics, augmented reality, and 3D visual search.
Enabling open vocabulary 3D instance segmentation can significantly improve the flexibility and practicality of applications that rely on understanding and manipulating complex 3D scenes. let’s stay OpenMask3Dthe promising 3D instance segmentation model.
OpenMask3D aims to overcome the limitations of closed vocabulary approaches. It tackles the task of predicting masks of 3D object instances and calculating representations of mask features while reasoning beyond a predefined set of concepts. OpenMask3D it operates in RGB-D sequences and takes advantage of the corresponding 3D reconstructed geometry to achieve its objectives.
It uses a two-stage pipeline consisting of a class-independent skin proposal header and a skin feature aggregator module. OpenMask3D identifies frames where instances are obvious and extracts CLIP features from the best images of each mask. The resulting feature representation is aggregated across multiple views and associated with each 3D instance skin. This instance-based feature calculation approach equips OpenMask3D with the ability to retrieve object instance masks based on their similarity to any given text query, enabling segmentation of open vocabulary 3D instances and overcoming the limitations of closed vocabulary paradigms.
When calculating a mask function per object instance, OpenMask3D can retrieve object instance masks based on similarity to any given query, making it capable of open vocabulary 3D instance segmentation. Besides, OpenMask3D retains information about novelty and long-tailed objects better than its trained or tuned counterparts. It also overcomes the limitations of a closed vocabulary paradigm, allowing segmentation of object instances based on free-form queries related to object properties such as semantics, geometry, performance, and material properties.
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Ekrem Çetinkaya received his B.Sc. in 2018, and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She received her Ph.D. He graduated in 2023 from the University of Klagenfurt, Austria, with his dissertation titled “Video Coding Improvements for HTTP Adaptive Streaming Using Machine Learning”. His research interests include deep learning, computer vision, video encoding, and multimedia networking.
