ESCAPE: Equivariant Shape Completion via Anchor Point Encoding
This addresses a crucial bottleneck in 3D computer vision for applications like robotics and augmented reality, offering a significant improvement over prior methods that rely on known poses.
The paper tackles the problem of shape completion under varying rotations, which limits real-world applicability of existing methods, and introduces ESCAPE, a novel framework that achieves robust, high-quality reconstructions across arbitrary rotations and translations without requiring pose estimation.
Shape completion, a crucial task in 3D computer vision, involves predicting and filling the missing regions of scanned or partially observed objects. Current methods expect known pose or canonical coordinates and do not perform well under varying rotations, limiting their real-world applicability. We introduce ESCAPE (Equivariant Shape Completion via Anchor Point Encoding), a novel framework designed to achieve rotation-equivariant shape completion. Our approach employs a distinctive encoding strategy by selecting anchor points from a shape and representing all points as a distance to all anchor points. This enables the model to capture a consistent, rotation-equivariant understanding of the object's geometry. ESCAPE leverages a transformer architecture to encode and decode the distance transformations, ensuring that generated shape completions remain accurate and equivariant under rotational transformations. Subsequently, we perform optimization to calculate the predicted shapes from the encodings. Experimental evaluations demonstrate that ESCAPE achieves robust, high-quality reconstructions across arbitrary rotations and translations, showcasing its effectiveness in real-world applications without additional pose estimation modules.