Learning Rotation-Invariant 3D Semantic Correspondence via Dynamic Local Shape Transform

Khái niệm cốt lõi

RIST learns to dynamically formulate an SO(3)-invariant local shape transform for each point, which maps the SO(3)-equivariant global shape descriptor of the input shape to a local shape descriptor. This enables RIST to establish dense point-wise correspondences between arbitrarily rotated 3D shapes.

Tóm tắt

The paper introduces RIST, a novel self-supervised approach for determining dense SO(3)-invariant correspondences between arbitrarily aligned 3D objects. The key idea is to formulate the local shape information of each point as a novel function called local shape transform with dynamic input-dependent parameters, which effectively maps the global shape descriptor of input shapes to local shape descriptors.
The main components of RIST are:

SO(3)-Equivariant Encoder: RIST uses VN-DGCNN as the encoder to extract an SO(3)-equivariant global shape descriptor and dynamic SO(3)-invariant point-wise local shape transforms.
SO(3)-Equivariant Decoder: The decoder is designed using SO(3)-equivariant layers to reconstruct the input shapes in a rotation-equivariant manner, using the global shape descriptor and local shape transforms.
Self-Supervised Training: RIST is trained in a self-supervised manner by penalizing errors in self- and cross-reconstruction of input point clouds. This encourages the local shape transforms to capture generalizable local semantics and geometry, enabling dense correspondence establishment.

The paper demonstrates that RIST achieves state-of-the-art performance on 3D part segmentation label transfer and 3D keypoint transfer under arbitrary rotations, outperforming existing methods by significant margins. This showcases RIST's potential for applications in computer vision that require robust 3D correspondence establishment.

Thống kê

"Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics."
"Existing self-supervised methods for this problem assume perfect input shape alignment, restricting their real-world applicability."
"RIST demonstrates state-of-the-art performances on 3D part label transfer and semantic keypoint transfer given arbitrarily rotated point cloud pairs of the same category, outperforming existing methods by significant margins."

Trích dẫn

"Establishing dense 3D correspondences between different shapes is foundational to numerous applications across computer vision, graphics, and robotics [9, 22, 28, 41]."
"One of the primary challenges hindering advancements in this domain is the difficulty of annotating dense inter-shape correspondences, which limits the leverage of strongly-supervised learning paradigms."
"To address this challenge, we introduce a novel self-supervised learning approach, dubbed RIST, designed to reliably determine dense SO(3)-invariant correspondences between shapes via local shape transform."

Thông tin chi tiết chính được chắt lọc từ

Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform

by Chunghyun Pa... lúc arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11156.pdf

Yêu cầu sâu hơn

How can RIST's dynamic local shape transforms be extended to handle non-rigid deformations between shapes

RIST's dynamic local shape transforms can be extended to handle non-rigid deformations between shapes by incorporating additional mechanisms to capture the deformations. One approach could be to introduce learnable parameters in the local shape transforms that can adapt to the varying deformations between shapes. By allowing the local shape transforms to adjust based on the specific deformations present in the shapes, RIST can better align and establish correspondences between non-rigidly deformed shapes. Additionally, integrating techniques from non-rigid registration methods or deformation models can enhance the ability of RIST to handle complex deformations.

What are the potential limitations of RIST in handling topological changes between shapes, and how could this be addressed

One potential limitation of RIST in handling topological changes between shapes is the reliance on local shape transforms that may struggle with drastic topological differences. In cases where shapes undergo significant topological changes, such as parts being added or removed, the local shape transforms may not effectively capture the necessary information for establishing correspondences. To address this limitation, incorporating topological awareness into the local shape transforms or integrating topological constraints during the correspondence establishment process can help improve the handling of topological changes. Additionally, leveraging hierarchical representations or incorporating topological descriptors can enhance the robustness of RIST to topological variations.

Can the principles of RIST be applied to establish correspondences between 2D images or other modalities beyond 3D point clouds

The principles of RIST can be applied to establish correspondences between 2D images or other modalities beyond 3D point clouds by adapting the architecture and mechanisms to suit the specific characteristics of the new modality. For 2D images, the local shape transforms can be modified to capture spatial relationships and features in the images, enabling the establishment of dense correspondences. Techniques such as spatial transformers or attention mechanisms can be incorporated to handle the 2D nature of the data. Similarly, for other modalities, such as videos or sensor data, the concept of dynamic local transforms can be extended to capture the temporal or domain-specific features for correspondence establishment. By customizing the approach to the unique properties of each modality, RIST can be applied effectively to a wide range of data types for semantic correspondence tasks.