Depth and pose estimation are critical for enabling machines to interact effectively with the real world. Depth estimation provides the spatial structure of a scene, pose estimation localises and orients objects within it, both fundamental for robotics, augmented reality, and 3D understanding. Traditional approaches achieved impressive results on standard benchmarks like KITTI and Middlebury. However, when these methods encounter reflective and transparent objects, their performance degrades significantly. This limitation is particularly problematic as these challenging materials are common in everyday environments. TRICKY 2025 features two complementary challenges encouraging the development of next-generation algorithms capable of advanced reasoning on non-Lambertian objects.