The field of 6D pose estimation and visual localization is moving towards more robust and flexible methods that can handle novel objects and unseen environments. Researchers are exploring new approaches that eliminate the need for retraining or large databases, and instead focus on developing models that can learn from limited data and generalize well to new scenarios. Noteworthy papers in this area include: Any6D, which introduces a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image. Scene-agnostic Pose Regression for Visual Localization, which proposes a new task that achieves accurate pose regression in a flexible way without retraining or databases. Reasoning and Learning a Perceptual Metric for Self-Training of Reflective Objects, which presents a two-stage framework for bin-picking of metal objects using low-cost RGB-D cameras. DINeMo, which learns neural mesh models with no 3D annotations by leveraging pseudo-correspondence obtained from large visual foundation models.