Robust 3D-aware Object Classification via Discriminative Render-and-Compare

In real-world applications, it is essential to jointly estimate the 3D object pose and class label of objects, i.e., to perform 3D-aware classification.While current approaches for either image classification or pose estimation can be extended to 3D-aware classification, we observe that they are inherently limited: 1) Their performance is much lower compared to the respective single-task models, and 2) they are not robust in out-of-distribution (OOD) scenarios. Our main contribution is a novel architecture for 3D-aware classification, which builds upon a recent work and performs comparably to single-task models while being highly robust. In our method, an object category is represented as a 3D cuboid mesh composed of feature vectors at each mesh vertex. Using differentiable rendering, we estimate the 3D object pose by minimizing the reconstruction error between the mesh and the feature representation of the target image. Object classification is then performed by comparing the reconstruction losses across object categories. Notably, the neural texture of the mesh is trained in a discriminative manner to enhance the classification performance while also avoiding local optima in the reconstruction loss. Furthermore, we show how our method and feed-forward neural networks can be combined to scale the render-and-compare approach to larger numbers of categories. Our experiments on PASCAL3D+, occluded-PASCAL3D+, and OOD-CV show that our method outperforms all baselines at 3D-aware classification by a wide margin in terms of performance and robustness

OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area. Our dataset can be accessed from https://bzhao.me/OOD-CV/Comment: arXiv admin note: substantial text overlap with arXiv:2111.1434