Interactive 3D Segmentation via Attention-based User Clicks

Основные понятия

iSeg is a data-driven interactive technique for 3D shape segmentation that generates tailored partitions of the shape according to user clicks.

Аннотация

The paper presents iSeg, a new interactive technique for segmenting 3D shapes. Previous works have focused mainly on leveraging pre-trained 2D foundation models for 3D segmentation based on text, which may be insufficient for accurately describing fine-grained spatial segmentations. Moreover, achieving a consistent 3D segmentation using a 2D model is challenging since occluded areas of the same semantic region may not be visible together from any 2D view.

iSeg's key components are:

• Mesh Feature Field (MFF): A function that embeds each mesh vertex into a deep feature vector, distilling semantic information from a pre-trained 2D foundation model.
• Interactive Attention Layer: A novel attention mechanism that can handle a variable number of user clicks, both positive and negative, to indicate regions to include or exclude.
• 3D Segmentation Decoder: A network that takes the MFF and the user clicks and predicts the corresponding mesh segmentation.

The training of iSeg leverages the 2D foundation model to provide supervision, but the final segmentation is computed directly in 3D, ensuring view-consistency. iSeg is shown to be highly versatile, working on a variety of shapes and geometries, and faithful to the user's specifications, outperforming alternative interactive 3D segmentation methods.

Статистика

None

Цитаты

None

Ключевые выводы из

iSeg

by Itai Lang,Fe... в arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03219.pdf

Дополнительные вопросы

How could iSeg's interactive attention mechanism be extended to handle more complex user inputs, such as scribbles or freeform sketches

To extend iSeg's interactive attention mechanism to handle more complex user inputs like scribbles or freeform sketches, we can introduce additional layers or modules in the network architecture. One approach could be to incorporate a sketch processing module that can interpret freeform sketches or scribbles provided by the user. This module could preprocess the input sketches to extract relevant features and convert them into a format that can be integrated into the existing interactive attention mechanism. By incorporating a sketch understanding component, iSeg can adapt to a wider range of user inputs, allowing for more flexible and intuitive interactions with the 3D shapes. Additionally, leveraging techniques from image segmentation and sketch recognition domains could enhance the model's ability to interpret and respond to diverse user inputs effectively.

How might iSeg's 3D-consistent segmentation be leveraged for other 3D shape analysis and editing tasks beyond interactive segmentation

The 3D-consistent segmentation capability of iSeg can be leveraged for various other 3D shape analysis and editing tasks beyond interactive segmentation. One potential application is in 3D shape completion, where the model can predict missing parts of a shape based on the existing structure and user inputs. By maintaining consistency in the 3D space, iSeg can ensure that the completed shapes are coherent and realistic. Furthermore, the 3D-consistent segmentation can be utilized in tasks like shape correspondence, shape retrieval, and shape editing. For instance, the model can assist in identifying corresponding parts across different shapes or in editing specific regions of a shape while preserving its overall structure. Overall, the 3D-consistent segmentation capability opens up possibilities for a wide range of 3D shape analysis and editing tasks.

Could the distillation of 2D foundation model features to a 3D mesh representation be applied to other 3D understanding problems beyond segmentation

The distillation of 2D foundation model features to a 3D mesh representation, as demonstrated in iSeg, can be applied to various other 3D understanding problems beyond segmentation. One potential application is in 3D object recognition and classification, where the distilled features can be used to enhance the representation of 3D objects for accurate recognition tasks. By leveraging the rich semantic information encoded in the 2D features and transferring it to the 3D domain, the model can improve the understanding and classification of complex 3D shapes. Additionally, the distilled features can be utilized in tasks like 3D shape reconstruction, shape generation, and shape retrieval, enabling more robust and efficient solutions in these domains. The approach of distilling knowledge from 2D models to enhance 3D understanding can significantly benefit a wide range of 3D-related tasks beyond segmentation.