Self-Supervised Viewpoint Learning From Image Collections (2004.01793v1)

Abstract: Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabelled images of an object category from the internet, e.g., of cars or faces. We seek to answer the research question of whether such unlabeled collections of in-the-wild images can be successfully utilized to train viewpoint estimation networks for general object categories purely via self-supervision. Self-supervision here refers to the fact that the only true supervisory signal that the network has is the input image itself. We propose a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise our viewpoint estimation network. We show that our approach performs competitively to fully-supervised approaches for several object categories like human faces, cars, buses, and trains. Our work opens up further research in self-supervised viewpoint learning and serves as a robust baseline for it. We open-source our code at https://github.com/NVlabs/SSV.

• The paper presents a self-supervised method for viewpoint estimation that eliminates the need for manual annotations.
• It utilizes a generative synthesis network with cycle-consistency and adversarial learning to achieve competitive results on diverse objects such as faces and vehicles.
• The findings demonstrate reduced annotation dependency and promising applications in autonomous driving, AR/VR, and robotics.

Self-Supervised Viewpoint Learning From Image Collections

The paper "Self-Supervised Viewpoint Learning From Image Collections" addresses a critical problem in computer vision: estimating the viewpoint of objects from images without requiring manually labeled data. The core of the research examines if unannotated collections of images can be effectively utilized to train neural networks for object viewpoint estimation via self-supervision.

Self-supervised learning presents a promising direction, particularly as it offers a remedy to the challenges associated with annotated datasets. The paper proposes an innovative framework that leverages a generative network to synthesize images in a viewpoint-aware manner, employing analysis-by-synthesis. This synthesis is combined with symmetry and adversarial constraints to effectively train the viewpoint estimation network.

The framework is tested across a range of objects, including human faces, cars, buses, and trains, showing competitive performance when compared to fully-supervised methodologies. The paper's results suggest that the proposed self-supervised method significantly reduces the dependency on costly data annotations while demonstrating strong empirical results.

Methodology Overview

The proposed methodology integrates several components:

• Analysis-by-Synthesis Paradigm: This approach uses a generative synthesis network to render images, with a focus on reconstructive cycles to ensure viewpoint accuracy. The network learns to synthesize images conditioned on input parameters representing style and viewpoint, thus enabling self-supervision for viewpoint regression.
• Cycle-Consistency Losses: These losses enforce consistency between the input image and the reconstructed image generated by the synthesis network, thereby supervising the training of the viewpoint estimation network.
• Symmetry Constraint: By leveraging the inherent symmetry present in many object categories, the approach enforces symmetry conditions on viewpoint predictions to further refine the accuracy.
• Adversarial Learning: The viewpoint network also acts as a discriminator, distinguishing real from synthetic images, which enhances the realism of generated image samples in training.

Experimental Validation and Results

The paper's experimental results demonstrate the framework's robustness and efficacy:

• Head Pose Estimation: On the BIWI dataset for head pose estimation, the self-supervised approach achieved competitive mean absolute errors compared to several fully and partially supervised baselines, highlighting the usefulness of the proposed generative consistency and symmetry constraints.
• Generalization to Additional Object Categories: Beyond faces, the framework is effectively extended to other categories like cars, buses, and trains, using large unlabelled datasets from CompCars and OpenImages.
• Performance Against Supervised Models: While not surpassing all supervised methods, the self-supervised approach provides a substantial decrease in viewpoint error rates. Specifically, it achieves results comparable to certain supervised approaches, presenting a compelling case for self-supervised learning in viewpoint estimation tasks.

Implications and Future Directions

The presented self-supervised framework paves the way for broader applications in fields requiring 3D understanding from 2D imagery. The successful application of this framework could facilitate advancements in autonomous driving (through better object orientation understanding), AR/VR technologies, and robotic perception systems. Moreover, the methodology's capability to function without ground-truth annotations crucially contributes to its applicability in less-scraped data domains.

Future work will likely explore integrating more diverse constraints or additional self-supervised signals to further increase accuracy and domain robustness. Another potential research avenue could involve expanding the coverage to other object categories and scene topologies, testing the universality of the approach on varied data and under varied conditions.

Ultimately, the paper demonstrates that self-supervised learning holds strong promise for efficient 3D viewpoint estimation, prompting both theoretical exploration and practical development in AI and computer vision.