We describe a new multiscale keypoint detector and a set of local visual descriptors, both based on the efficient Dual-Tree Complex Wavelet Transform. The detector has properties and performance similar to multiscale Förstner-Harris detectors. The descriptor provides efficient rotation-invariant matching. We evaluate the method, comparing it to a previous wavelet based approach and to several conventional detectors and descriptors on a new dataset designed for the automatic evaluation of 3D viewpoint invariance. The dataset contains over 4000 images of toy cars on a turntable under accurately calibrated conditions. Both it and the evaluation software are publicly available. Overall the method gives performance competitive with existing Harris-like detectors.