Search Results for author:
Found 5 papers, 3 papers with code
Mesh2Tex: Generating Mesh Textures from Image Queries
The learned texture manifold enables effective navigation to generate an object texture for a given 3D object geometry that matches to an input RGB image, which maintains robustness even under challenging real-world scenarios where the mesh geometry approximates an inexact match to the underlying geometry in the RGB image.
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans
3D object recognition has seen significant advances in recent years, showing impressive performance on real-world 3D scan benchmarks, but lacking in object part reasoning, which is fundamental to higher-level scene understanding such as inter-object similarities or object functionality.
Towards Part-Based Understanding of RGB-D Scans
Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-grained understanding is required to enable interactions with objects and their functional understanding.
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios.
Boundary Loss for Remote Sensing Imagery Semantic Segmentation
Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data.
