LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation (1912.00416v3)

Abstract: Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a result, they are difficult to scale to a large number of objects and cannot be directly applied to unseen objects. We propose a novel framework for 6D pose estimation of unseen objects. We present a network that reconstructs a latent 3D representation of an object using a small number of reference views at inference time. Our network is able to render the latent 3D representation from arbitrary views. Using this neural renderer, we directly optimize for pose given an input image. By training our network with a large number of 3D shapes for reconstruction and rendering, our network generalizes well to unseen objects. We present a new dataset for unseen object pose estimation--MOPED. We evaluate the performance of our method for unseen object pose estimation on MOPED as well as the ModelNet and LINEMOD datasets. Our method performs competitively to supervised methods that are trained on those objects. Code and data is available at https://keunhong.com/publications/latentfusion/.

• The paper presents an end-to-end differentiable framework that reconstructs latent 3D representations from a few reference images for unseen object pose estimation.
• It leverages neural rendering and gradient-based optimization to synthesize views and accurately align object poses.
• Experiments on LINEMOD, ModelNet, and MOPED show competitive results compared to supervised methods with minimal reference images.

An Overview of LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation

In the paper "LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation," the authors propose a novel framework aimed at addressing the challenge of 6D pose estimation for objects that are not seen during training. Traditional methods require a 3D model for each object and necessitate retraining the model to incorporate new objects. Such requirements pose significant scalability issues, preventing the direct application of these methods to unseen objects. The authors introduce a technique wherein a neural network learns to reconstruct a latent 3D representation of an object using reference views at inference time, facilitating object pose estimation without retraining.

Technical Contributions

The proposed method consists of the following core components:

1. Latent Representation: By utilizing a neural network, the authors reconstruct a latent 3D representation from a handful of reference images, circumventing the need for complete 3D models often acquired using expensive and labor-intensive scanning processes.
2. Neural Rendering: The network functions as a differentiable renderer capable of generating arbitrary views from the latent 3D representation, allowing the view synthesis required for pose estimation.
3. Gradient-Based Pose Optimization: For pose estimation, the method involves direct optimization of the pose using gradient descent based on the rendered views compared with the input using a novel latent space loss.

Through training on a broad array of 3D shapes under various lighting conditions and with random texturing, the network achieves generalization to categories not seen during training. The authors introduce the Model-free Object Pose Estimation Dataset (MOPED) for evaluating such zero-shot settings and benchmark their method against datasets like ModelNet and LINEMOD.

Results and Analysis

The performance of this approach was examined on different datasets. On the LINEMOD dataset, the model showed competitive results when compared to supervised methods that are specifically trained on those datasets. The experiments on the ModelNet validated the model's ability to generalize to novel object categories. Meanwhile, the MOPED dataset, designed for evaluating zero-shot object pose estimation, further demonstrated the robustness and applicability of the proposed method.

Key findings include:

• The approach performs comparably to existing supervised methods while requiring no retraining for novel objects.
• A major advantage is the method's requirement for only a few reference images, supporting scalability and practicality in real-world applications.
• The robustness of the latent-space representation leverages features from multi-view coherence, enabling accurate and efficient pose estimation.

Theoretical and Practical Implications

Theoretically, this paper advances the understanding of 3D latent space representations and their utility in object pose estimation. The framework demonstrates the potential of machine learning models to circumvent the need for object-specific training data, which is pivotal for scaling machine learning solutions in diverse environments.

Practically, the ability to perform real-time pose estimation under zero-shot scenarios holds promise for applications in robotics and augmented reality. Robots can be deployed in novel environments without the prerequisite of extensive training periods, allowing for more dynamic and adaptable interactions with their environments.

Future Directions

The authors suggest future work such as improving object pose estimation in cluttered and occluded scenes, increasing processing speed through network optimization, and exploring the scalability of this approach to interactively extend into more complex operational settings. The integration of more advanced segmentation approaches and incorporating additional sensory inputs like semantics could also enhance the robustness and versatility of the proposed framework.

In summary, the paper presents a significant contribution to the field of computer vision by demonstrating an innovative zero-shot approach to pose estimation through end-to-end differentiable neural rendering, setting a foundation for seamless integration of new objects into machine learning systems without the recursive cycle of training and retraining.