Unsupervised category-level viewpoint estimation

The recent progress in deep learning techniques transformed the field of computer vision, with tasks like object classification or segmentation being almost considered solved. This however requires sufficiently many labeled samples to train the system, hence research focus has shifted towards tasks where collecting such data is challenging. Recovering camera poses is one such task, where labels are typically too costly for supervised approaches. This work explores solutions to train camera pose estimation systems without the need for external supervision. Preliminary assessments show that it is possible to formulate this problem as a self supervised reconstruction task. By interpreting a network output as 3D rotation, and using this output to control a differentiable rendering operation, gradient descent can be used to train the network to predict viewpoint information. However, multiple issues arise when applying such a method naively on complex data. Confounding factors of particular importance are symmetries, geometry-breaking rendering pipelines and background induced noise. This leads to a regime where purely self-supervised training breaks, al though semi-supervised approaches are still successful. Specific solutions to the aforementioned problems are therefore studied and evaluated. For symmetries, multiple viewpoint predictions are made, and their distribution is further regulated. Two main rendering pipelines are also compared to improve over naive convolution-based reconstruction: a voxel-based one, and a more recent implicit neural representation. Experimental evidence shows that carefully crafting a system with these improvements allows recovery of poses on many everyday objects, such as cars and chairs, with performances reaching the level of supervised approaches on some categories. In addition, this thesis underlines two potential problems in related approaches. First, an unstable pose retrieval method used in recent implicit representations, that is prohibitively expensive. Second, an insidious issue in unsupervised methods, arising from a combination of dataset biases and naive calibration. As this potentially leads to overestimated performances, it calls for a more robust evaluation standard, as well as more careful data gathering