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Found 5 papers, 1 papers with code
Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis
Inverse rendering aims to reconstruct the scene properties of objects solely from multiview images.
DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets
Data imbalance is a well-known issue in the field of machine learning, attributable to the cost of data collection, the difficulty of labeling, and the geographical distribution of the data.
LANe: Lighting-Aware Neural Fields for Compositional Scene Synthesis
Specifically, we learn a scene representation that disentangles the static background and transient elements into a world-NeRF and class-specific object-NeRFs to allow compositional synthesis of multiple objects in the scene.
SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks
To the best of our knowledge, there is no prior work on scene relighting leveraging explicit geometric representations from a single image.
Deflating Dataset Bias Using Synthetic Data Augmentation
Deep Learning has seen an unprecedented increase in vision applications since the publication of large-scale object recognition datasets and introduction of scalable compute hardware.
