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Found 12 papers, 4 papers with code
Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGB
3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning.
Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditions
Neural field methods, initially successful in the inverse rendering domain, have recently been extended to CT reconstruction, marking a paradigm shift from traditional techniques.
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar
3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions.
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.
Towards Viewpoint Robustness in Bird's Eye View Segmentation
We introduce a technique for novel view synthesis and use it to transform collected data to the viewpoint of target rigs, allowing us to train BEV segmentation models for diverse target rigs without any additional data collection or labeling cost.
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.
Self-Supervised Feature Extraction for 3D Axon Segmentation
Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels.
Feature Forwarding for Efficient Single Image Dehazing
Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems.
