Search Results for author:
Found 8 papers, 2 papers with code
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images
It learns photorealistic scene representations by decomposing users' 3D views into personal and global NeRFs and a novel optimally weighted aggregation of only the latter.
SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition
3D modeling from satellite imagery is essential in areas of environmental science, urban planning, agriculture, and disaster response.
DISeR: Designing Imaging Systems with Reinforcement Learning
In this paper, we formulate these four building blocks of imaging systems as a context-free grammar (CFG), which can be automatically searched over with a learned camera designer to jointly optimize the imaging system with task-specific perception models.
ORCa: Glossy Objects As Radiance-Field Cameras
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
ORCa: Glossy Objects as Radiance Field Cameras
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging
Cameras were originally designed using physics-based heuristics to capture aesthetic images.
Physically Disentangled Representations
In this paper, we show the utility of inverse rendering in learning representations that yield improved accuracy on downstream clustering, linear classification, and segmentation tasks with the help of our novel Leave-One-Out, Cycle Contrastive loss (LOOCC), which improves disentanglement of scene parameters and robustness to out-of-distribution lighting and viewpoints.
Towards Learning Neural Representations from Shadows
We observe that shadows are a powerful cue that can constrain neural scene representations to learn SfS, and even outperform NeRF to reconstruct otherwise hidden geometry.
