Source-linked AI summary
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
Xu Chen, Zijian Dong, Jie Song, Andreas Geiger, Otmar Hilliges
TL;DR
Object pose estimation often requires explicit CAD models for individual instances, limiting category-level use. The paper combines category-level pose-aware neural image synthesis with gradient-based fitting, achieving orientation estimation from RGB and full 6DoF recovery with RGB-D.
Problem
Existing analysis-by-synthesis pose estimators require knowing the object instance and accessing an explicit representation such as a 3D CAD model.
Method
A pose-aware neural image generator represents category-level appearance, shape and pose, while gradient-based fitting jointly optimizes pose, shape and appearance against a target image.
Results
The approach reaches performance on par with and sometimes surpasses a strong RGB-D baseline, while RGB-D input recovers full 6DoF pose.
Takeaways & Limitations
Category-level neural analysis-by-synthesis can estimate object orientation from RGB images without instance-specific CAD models and extend to full 6DoF pose with depth.
Takeaways & Limitations
The method assumes the object is segmented from the background and currently focuses on rigid objects.
Abstract
from arXiv · showhide
Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module that is capable of implicitly representing the appearance, shape and pose of entire object categories, thus rendering the need for explicit CAD models per object instance unnecessary. The image synthesis network is designed to efficiently span the pose configuration space so that model capacity can be used to capture the shape and local appearance (i.e., texture) variations jointly. At inference time the synthesized images are compared to the target via an appearance based loss and the error signal is backpropagated through the network to the input parameters. Keeping the network parameters fixed, this allows for iterative optimization of the object pose, shape and appearance in a joint manner and we experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone. When provided with depth measurements, to overcome scale ambiguities, the method can accurately recover the full 6DOF pose successfully.
1 Introduction
The paper replaces instance-specific CAD representations with a pose-aware neural synthesis module integrated into gradient-based fitting. It estimates orientation from RGB images and recovers full 6DoF pose with RGB-D input, matching or sometimes surpassing a strong RGB-D baseline.
- 1 Introduction: Neural representations implicitly model category-level shape and appearance variation, removing the need for explicit instance-specific object models.The pose-aware network generates images with controllable poses, shapes and appearances and can extrapolate beyond its training set.
- 1 Introduction: The approach uses appearance-based discrepancies and backpropagated gradients to iteratively optimize pose and shape while keeping trained network weights fixed.This enables model fitting against the target image during inference.
- 1 Introduction: RGB inputs yield 3D orientation accuracy on par with or sometimes better than a state-of-the-art RGB-D method.The experiments compare against NOCS, which uses RGB-D input and paired pose annotations during training.
- 1 Introduction: The method jointly recovers object pose, shape and appearance by fitting a neural image generator to a single RGB or RGB-D image.The synthesis module is integrated into an optimization-based model-fitting framework.
- 1 Introduction: The experiments report parity with or occasional improvement over a strong RGB-D baseline and significant improvement over discriminative pose regression.The result summary also states that the generative model significantly outperforms a discriminative RGB-based baseline.
2 Related Work
Prior pose-estimation methods commonly depend on instance-specific CAD models, while related generative approaches have limitations in test-time inputs, pose control, or appearance modeling. This work instead uses category-level pose-conditioned appearance generation for fitting.
- 2 Related Work: Template matching and regression methods commonly require instance-specific 3D CAD models during training and testing, limiting applicability when models are impractical or difficult to capture.The cited limitations include impractical storage and comparison of all possible models and difficulty obtaining complete, high-fidelity geometry.
- 2 Related Work: NOCS reconstructs a canonical 3D model before aligning it to depth, whereas this method directly synthesizes appearance while jointly recovering pose, shape and appearance.The paper contrasts its fully generative pipeline with NOCS's intermediate 3D reconstruction step.
- 2 Related Work: Novel-view and disentangled-generation methods can produce different poses, but their shape or appearance control is not suitable for precise absolute pose estimation.The paper addresses this by integrating precise pose information into a conditional VAE latent representation.
- 2 Related Work: The paper's generator is trained in a VAE framework using reconstruction loss and KL divergence, then used for pose-aware synthesis and fitting.Its overview describes training on multi-view synthetic objects and inference by optimizing against a segmented real image.
- 2 Related Work: Category-level geometric generators model shape but not appearance, so they require depth measurements and discard information in object appearance.The proposed approach instead focuses on pose-conditioned appearance generation without intermediate geometry estimation.
3 Method
The method replaces instance-specific CAD models with a pose-aware neural image generator that represents category-level shape and appearance, then fits pose and latent appearance parameters by differentiable optimization. It efficiently models out-of-plane rotation with a 3D generator and handles translation and in-plane rotation through 2D warping.
- A learned image synthesis module represents category-level object shape, appearance, and pose, enabling pose estimation without instance-specific 3D CAD models.The generator is trained on synthetic multi-view objects and later used for unseen instances.
- 3.1 Pose-aware Image Generator: The generator factorizes pose by learning out-of-plane rotations in 3D while applying translation and in-plane rotation as 2D similarity transformations.This design avoids learning the full 6DoF transformation space directly, preserving capacity for shape and appearance variation.
- 3.2 Training: A conditional VAE combines pose labels with latent codes so the decoder can generate the same object instance in a desired pose with controlled shape and appearance variation.The encoder and decoder are jointly trained with reconstruction loss regularized by KL divergence using rendered ShapeNet data.
- 3.3 Object Pose Estimation: At inference, the fixed differentiable generator is fitted by iteratively refining pose and latent code to minimize perceptual discrepancy between the target and synthesized images.The appearance loss uses VGG features and includes latent-code regularization to discourage degenerate solutions.
- 3.3 Object Pose Estimation: The method assumes that the object is segmented from the background before pose and appearance optimization.The paper suggests obtaining this segmentation with off-the-shelf networks such as Mask-RCNN.
- 3.3 Object Pose Estimation: Multiple initial poses and latent codes are optimized in parallel because the non-convex energy can contain local minima.Initial poses are sampled uniformly, while latent codes are sampled from the encoder’s conditional Gaussian distribution.
4 Evaluation
The evaluation compares the method with NOCS and discriminative baselines, then analyzes network, loss, optimization, and robustness choices. RGB recovers orientation competitively, depth resolves translation scale ambiguity, and design choices affect fitting quality.
- Comparison with state-of-the-art: With depth, translation performance is comparable to NOCS, whereas RGB-only translation has higher error because 2D observations contain scale ambiguities.Depth is treated as an additional color channel, with translation parameter Tz added directly to the generated depth map.
- Comparison with state-of-the-art: Using RGB only, the method achieves orientation results on par with or better than NOCS, outperforming it on bottle, can, and camera categories.NOCS uses RGB-D input and real images with paired pose annotations during training.
- Comparison with state-of-the-art: The method significantly outperforms a discriminative RGB-based orientation regressor trained on the same synthetic data.The authors attribute part of the gap to discriminative sensitivity to distribution shifts and domain gaps.
- Component Analysis: Removing the 3D feature volume or VAE regularization harms image generation and fitting, while excessively high- or low-dimensional latent spaces also degrade pose estimation.The 3D feature volume supports faithful rotation, and KL regularization provides an informative latent-space gradient.
- Component Analysis: Perceptual loss outperforms L1, L2, and SSIM, and decreasing energy corresponds to decreasing rotational error during optimization.The perceptual loss encourages global semantic alignment rather than local pixel-wise alignment.
- Robustness: The generative method is less sensitive than the discriminative model to gaps between training and test instances when shape, appearance, and pose variation exceed the training distribution.This robustness is evaluated by varying three factors without augmented images during training.
5 Conclusion
The paper presents a category-level pose-estimation method that combines gradient-based fitting with a parametric neural image synthesis model, avoiding instance-specific CAD models at test time.
- The method implicitly represents the appearance, shape, and pose of entire object categories, avoiding instance-specific 3D CAD models at test time.
- The approach reaches performance on par with and sometimes better than a strong RGB-D baseline.
- The paper focuses on rigid objects and identifies extending the method to non-rigid or partially rigid objects as future work.