MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation

Khái niệm cốt lõi

MRC-Net proposes a novel approach to estimate the 6-DoF pose of objects using a sequential pipeline with multi-scale residual correlation, outperforming existing methods on challenging datasets.

Tóm tắt

MRC-Net introduces a two-stage deep learning pipeline for pose estimation from RGB images. The method involves classification and regression stages, connected by a multi-scale residual correlation layer. Soft probabilistic labels are used to define pose classes, reducing ambiguity in classification for symmetric objects. The network is end-to-end trainable and achieves state-of-the-art accuracy on various benchmark datasets. Experiments show that the sequential approach significantly boosts performance compared to parallel methods. The multi-scale residual correlation captures correspondences between input and rendered images at different scales, improving discriminative features for pose estimation. Perspective correction enhances accuracy, while test-time augmentation further improves performance.

Thống kê

We propose a single-shot approach to determining 6-DoF pose of an object with available 3D CAD model from a single RGB image.
Our method demonstrates state-of-the-art accuracy on four challenging BOP benchmark datasets:
T-LESS, LM-O, YCB-V, and ITODD.
The overall network, dubbed MRC-Net is shown in Figure 1.
We validate our hypothesis in experiments, showing that the simple concept of sequential pose estimation with the MRC layer produces a major boost in performance and outperforms state-of-the-art techniques on the BOP Challenge datasets without the need for pre-initialization, iterative refinement, and post-processing.
To summarize, the main contributions of this work are:
• MRC-Net employs a Siamese network with shared weights between both stages to learn embeddings for input and rendered images.
• A novel MRC layer implicitly captures correspondences between input and rendered images at both global and local scales.
• State-of-the-art accuracy on a variety of BOP benchmark datasets, advancing average recall by 2.4% on average compared to results reported by competing methods.

Trích dẫn

Thông tin chi tiết chính được chắt lọc từ

MRC-Net

by Yuelong Li,Y... lúc arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08019.pdf

Yêu cầu sâu hơn

How can the proposed method be extended to incorporate depth maps for improved accuracy

To incorporate depth maps into the proposed MRC-Net for improved accuracy, we can introduce a parallel branch in the network architecture dedicated to processing depth information. This branch would take as input the RGB image crop along with its corresponding depth map. The depth map can provide valuable geometric information about the scene, aiding in better understanding object shapes and positions in 3D space. By integrating depth information into the network, we can enhance the model's ability to estimate object poses accurately by leveraging both appearance-based features from RGB images and spatial information from depth maps.
Additionally, we can modify the loss function to include terms that encourage consistency between predicted poses based on RGB inputs and those inferred from depth data. By jointly optimizing for pose estimation using both modalities, the model can leverage complementary cues to refine its predictions further. Furthermore, techniques such as multi-modal fusion or attention mechanisms can be employed to effectively combine information from RGB images and depth maps at different stages of feature extraction and pose estimation within the network.

What are potential solutions to address degraded performance when CAD models are inaccurate

When dealing with inaccurate CAD models that lead to degraded performance in pose estimation tasks like those addressed by MRC-Net, several strategies can be implemented:

Data Augmentation: Introduce data augmentation techniques during training that simulate variations in object geometry or texture similar to real-world scenarios.

Model Ensembling: Train multiple instances of MRC-Net with diverse CAD models or perturbations of existing ones and ensemble their predictions at inference time for more robust results.

Fine-tuning on Real Data: Fine-tune MRC-Net on real-world images containing objects with accurate geometries to adapt it better for practical applications where CAD models may not align perfectly with reality.

Domain Adaptation Techniques: Utilize domain adaptation methods to bridge any gap between synthetic CAD models used during training and real-world objects encountered during testing.

By incorporating these strategies into the training pipeline of MRC-Net, it is possible to mitigate issues arising from inaccurate CAD models and improve overall performance under challenging conditions.

How does MRC-Net compare in terms of runtime efficiency with other state-of-the-art methods

In terms of runtime efficiency compared to other state-of-the-art methods for single-object pose estimation tasks like CIR [33], PFA [22], SurfEmb [13], GDR-Net [52], SC6D [2], etc., MRC-Net demonstrates competitive performance while offering superior accuracy:

MRC-Net Runtime: On average, MRC-Net takes approximately 61 milliseconds per inference task on a standard AWS EC2 P3 instance when processing a 256x256 image crop.

Comparison: While some iterative refinement methods like CIR require significantly longer runtimes (2542 ms), faster approaches like SC6D (25 ms) outperform MRC-Net slightly but sacrifice accuracy levels achieved by our method.

Overall, despite being slower than some direct regression methods due to its two-stage inference process involving rendering steps, MRC-Net strikes a balance between runtime efficiency and precision in single-object pose estimation tasks compared against various contemporary approaches available today