[Paper Review] Unsupervised Domain Adaptation for 3D Keypoint Prediction from a Single Depth Scan.

Xingyi Zhou, Arjun Karpur+3 more|arXiv (Cornell University)|Dec 15, 2017

Human Pose and Action RecognitionComputer Science43 references10 citations

TL;DR

This paper proposes an unsupervised domain adaptation method for 3D keypoint prediction from single depth scans by enforcing view consistency across multiple views of the same object and introducing a geometric alignment loss. The approach achieves superior performance over state-of-the-art domain adaptation techniques on real-world datasets through alternating optimization of the unified loss function.

ABSTRACT

In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan/image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency provides effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.

Motivation & Objective

• To address the challenge of domain shift in 3D keypoint prediction when training on synthetic data and testing on real depth scans.
• To leverage unlabeled real-domain data without requiring annotated keypoint labels by exploiting multi-view consistency.
• To improve generalization to real-world scenarios by introducing geometric regularization in the target domain.
• To develop a loss function that enables effective domain adaptation via alternating minimization.
• To demonstrate that view consistency and geometric alignment jointly enhance prediction robustness without supervision in the target domain.

Proposed method

• Utilizes multi-view predictions from the same 3D object to enforce consistency, treating consistent predictions as a form of self-supervision.
• Introduces a geometric alignment term that regularizes predicted keypoint locations to align with the underlying 3D geometry of the object.
• Combines view consistency and geometric alignment into a unified loss function optimized via alternating minimization.
• Leverages the fact that predictions from different views of the same object should be spatially consistent, even when views are from different angles.
• Uses the unlabeled target domain data to refine predictions by minimizing inconsistency across views and enforcing geometric plausibility.
• Employs a deep neural network for keypoint regression that is jointly trained using the combined loss on both source (synthetic) and target (real) domains.

Experimental results

Research questions

• RQ1 Can view consistency across multiple views of the same object serve as effective self-supervision for unsupervised domain adaptation in 3D keypoint prediction?
• RQ2 How does incorporating geometric alignment improve the robustness and accuracy of keypoint predictions in the target domain?
• RQ3 To what extent does the proposed method reduce domain shift compared to general-purpose domain adaptation techniques?
• RQ4 Can the combination of view consistency and geometric regularization outperform existing SOTA domain adaptation methods on real-world depth data?

Key findings

• The proposed method achieves superior performance on real-world datasets compared to state-of-the-art general-purpose domain adaptation techniques.
• View consistency across multiple views significantly improves prediction consistency and generalization in the absence of target domain annotations.
• The geometric alignment term enhances the plausibility of predicted keypoint locations, especially in challenging real-world scenarios.
• The alternating minimization strategy effectively optimizes the combined loss function, leading to stable convergence and improved performance.
• The method demonstrates strong zero-shot generalization from synthetic source data to real target data without requiring labeled target instances.
• Empirical results confirm that the combination of view consistency and geometric regularization leads to more accurate and robust 3D keypoint predictions.