-
Multiview Photometric Stereo
This paper addresses the problem of obtaining complete, detailed reconstructions of textureless shiny objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with previous photometric stereo techniques,…
-
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency
This paper presents a volumetric formulation for the multi-view stereo problem which is amenable to a computationally tractable global optimisation using Graph-cuts. Our approach is to seek the optimal partitioning of 3D space into two regions labelled as “object” and…
-
Automatic 3D object segmentation in multiple views using volumetric graph-cuts
-
Learning non-metric visual similarity for image retrieval
-
Video Normals from Colored Lights
We present an algorithm and the associated single-view capture methodology to acquire the detailed 3D shape, bends, and wrinkles of deforming surfaces. Moving 3D data has been difficult to obtain by methods that rely on known surface features, structured light,…
-
Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture
-
An Efficient Industrial System for Vehicle Tyre (Tire) Detection and Text Recognition Using Deep Learning
This paper addresses the challenge of reading low contrast text on tyre sidewall images of vehicles in motion. It presents first of its kind, a full scale industrial system which can read tyre codes when installed along driveways such as…
-
Overcoming Shadows in 3-Source Photometric Stereo
Light occlusions are one of the most significant difficulties of photometric stereo methods. When three or more images are available without occlusion, the local surface orientation is overdetermined so that shape can be computed and the shadowed pixels can be…
-
Learning an augmentation strategy for sparse datasets
-
A dual-aligned knowledge self-distillation framework for visible-infrared cross-modal person re-identification
• Dual alignment knowledge self-distillation to better capture modality-invariant/specific features for VI-ReID • Temperature-modulated alignment and confidence-based selective masking to enhance model reliability. • CutSwap augmentation to improve model robustness against intra-class variations and modality discrepancies. • State-of-the-art performance on…