NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation (2101.12378v3)

Abstract: 3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust when objects are partially occluded or viewed from a previously unseen pose. Inspired by the robustness of generative vision models to partial occlusion, we propose to integrate deep neural networks with 3D generative representations of objects into a unified neural architecture that we term NeMo. In particular, NeMo learns a generative model of neural feature activations at each vertex on a dense 3D mesh. Using differentiable rendering we estimate the 3D object pose by minimizing the reconstruction error between NeMo and the feature representation of the target image. To avoid local optima in the reconstruction loss, we train the feature extractor to maximize the distance between the individual feature representations on the mesh using contrastive learning. Our extensive experiments on PASCAL3D+, occluded-PASCAL3D+ and ObjectNet3D show that NeMo is much more robust to partial occlusion and unseen pose compared to standard deep networks, while retaining competitive performance on regular data. Interestingly, our experiments also show that NeMo performs reasonably well even when the mesh representation only crudely approximates the true object geometry with a cuboid, hence revealing that the detailed 3D geometry is not needed for accurate 3D pose estimation. The code is publicly available at https://github.com/Angtian/NeMo.

• The paper introduces NeMo, a neural mesh model that integrates 3D generative representations with contrastive learning for robust 3D pose estimation.
• It employs a render-and-compare strategy to align neural activations with mesh vertices, effectively mitigating challenges from occlusions and novel viewpoints.
• Experimental results on benchmark datasets show improved pose accuracy and resilience compared to conventional keypoint-based methods.

Analysis of NeMo: Neural Mesh Models for Robust 3D Pose Estimation

The paper presents NeMo (Neural Mesh Models), a novel approach for tackling the challenges in 3D pose estimation, particularly under conditions of partial occlusion and unseen poses. This work emerges as significant due to the limitations observed in standard deep learning methods, which frequently falter when facing occluded objects or unfamiliar viewpoints. NeMo introduces a robust alternative by amalgamating neural networks with 3D generative models to improve the adaptability and resilience of 3D pose estimation.

Methodology

The core innovation of NeMo lies in its integration of a neural architecture with 3D generative object models. Unlike conventional keypoint-based methods that suffer under sparse and obscured visual conditions, NeMo leverages a dense 3D mesh representation of objects that allows for more robust estimation through a render-and-compare strategy. Specifically, NeMo employs a generative model of neural feature activations at each vertex on a 3D mesh, utilizing differentiable rendering to align features from the input image with the mesh model. This process minimizes the reconstruction error between the targeted features and the model prediction.

To prevent local minima in optimization, contrastive learning is employed. This approach ensures that feature representations extracted from the mesh are distinct and resilient to intra-category variations such as color or shape. By maximizing feature distinction using contrastive learning, NeMo enhances robustness against distribution shifts, such as those caused by occlusion or new viewpoints.

Experimental Validation

NeMo demonstrates its robustness and adaptability through extensive experimentation on PASCAL3D+, occluded-PASCAL3D+, and ObjectNet3D datasets. The results illustrate that NeMo surpasses traditional deep learning methods in robustness to partial occlusion and performance across unseen poses while maintaining competitive accuracy on standard data without occlusion. Notably, NeMo effectively distinguishes between occluder and object features, improving the interpretability of occlusion conditions relative to traditional models.

Another interesting finding is that the accuracy of 3D pose estimation is only marginally affected when replacing detailed object geometries with crude approximations, such as cuboids. This suggests that detailed geometry may not be necessary for capturing critical pose information, a claim supported by the superior performance of NeMo-MultiCuboid and NeMo-SingleCuboid, which use simplified mesh representations.

Implications and Future Directions

The paper's contributions underscore the potential of generative neural models in advancing computer vision tasks such as 3D pose estimation. By implementing a feature generative model, NeMo offers a pathway to developing robust, adaptable computer vision systems that can handle real-world challenges like occlusion and novel viewpoints. The emphasis on contrastive learning further exemplifies the growing importance of robust feature representation in AI.

Looking forward, this research prompts several avenues for future exploration. Extending NeMo to adaptively refine its mesh representations based on contextual cues could enhance its robustness further. Additionally, incorporating more sophisticated scene recognition models to contextualize and predict occlusion could render the model even more effective across varied environments.

NeMo's principle of using a neural generative approach to bypass limitations inherent in classical models serves as a precedent for future innovations in AI robustness and adaptability in the field of 3D computer vision.