Learning Dense Correspondence via 3D-guided Cycle Consistency (1604.05383v1)

Abstract: Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For this task, although we do not know what the ground-truth is, we know it should be consistent across instances of that category. We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross-instance correspondences between pairs of images depicting objects of the same category. For each pair of training images we find an appropriate 3D CAD model and render two synthetic views to link in with the pair, establishing a correspondence flow 4-cycle. We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and real-to-synthetic correspondences that are cycle-consistent with the ground-truth. At test time, no CAD models are required. We demonstrate that our end-to-end trained ConvNet supervised by cycle-consistency outperforms state-of-the-art pairwise matching methods in correspondence-related tasks.

• The paper’s main contribution is a novel framework that leverages 3D-guided cycle consistency to learn dense correspondences without manual annotations.
• It employs real and synthetic image quartets to train an end-to-end deep network, achieving improved keypoint transfer rates with a PCK increase from 19.6% to 24.0%.
• The approach demonstrates potential for applications in 3D-augmented reality and robotics by effectively bridging the gap between synthetic and real-world data.

Learning Dense Correspondence via 3D-guided Cycle Consistency: An Expert Review

The paper "Learning Dense Correspondence via 3D-guided Cycle Consistency," proposes a novel method for establishing dense visual correspondence between different object instances, a challenge due to the difficulty of obtaining direct ground-truth data. The authors introduce a unique approach based on 3D-guided cycle consistency to derive a supervisory signal for training a convolutional neural network (ConvNet) to predict correspondences across visual data, bridging the gap between synthetic and real-world domains.

Overview of the Approach

The authors leverage the notion of cycle consistency to train the ConvNet without requiring any direct manual annotations for dense correspondences in real images. The methodology involves creating a cycle comprising real images and synthetic views of 3D CAD models. The key innovation lies in forming a correspondence flow that is cycle-consistent, such that synthetic-to-real, real-to-real, and real-to-synthetic correspondences are coherently established.

Using 3D CAD models from the ShapeNet repository, the authors generate training quartets consisting of two synthetic and two real-world images. The synthetic-to-synthetic correspondences are known by construction from these synthetic images, providing the supervisory signal needed. This enables the network to learn how to align these modalities effectively. The test phase dispenses with the requirement of CAD models, permitting direct application of the learned network to real-world tasks.

Key Contributions and Results

1. Meta-Supervision Framework: The paper introduces a general framework for learning tasks with no direct labels through the innovative use of cycle consistency as a meta-supervisory signal. This framework can potentially be adapted for other applications in computer vision, emphasizing tasks where obtaining ground truth is challenging.
2. End-to-End Learned Deep Network: The authors demonstrate one of the first successful implementations of an end-to-end trained deep network for dense cross-instance correspondence. This advancement significantly improves the capability over traditional methods such as SIFT flow, particularly on tasks involving substantial viewpoint and appearance variations.
3. Quantitative Improvements: The paper reports performance improvements in dense correspondence tasks. For instance, their approach surpasses existing methods, reaching a mean percentage of correct keypoint transfer (PCK) of 24.0% across various object categories in the PASCAL3D+ dataset, compared to 19.6% with SIFT flow.
4. Theoretical and Practical Implications: This research establishes that 3D CAD models, when combined with cycle consistency, can be a powerful tool in learning the latent structures necessary for solving dense visual correspondence tasks. It opens avenues for further exploration in exploiting the geometry of 3D data for enhancing other computer vision tasks, such as segmentation, recognition, and 3D reconstruction.

Implications and Future Directions

The theoretical underpinnings and empirical results suggest promising future directions in the exploration of cycle consistency as a broader framework for learning in weakly-supervised or unsupervised contexts. Practical applications could extend to improving 3D-augmented reality systems, better scene understanding in robotics, and enhanced performance in computational photography.

Furthermore, this method highlights the potential for using 3D model databases as rich sources of implicit supervision for a range of tasks across visual domains. Future research directions could include extending this framework to more complex scene understanding tasks or investigating its applicability across other modalities, such as video correspondence or cross-modal retrieval tasks.

In conclusion, the paper presents a robust step forward in the domain of dense visual correspondence, employing an innovative use of cycle consistency as a learning signal. It signifies a substantial contribution to the field, both in terms of immediate outcomes and potential for inspiring further research.