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Showing 1 to 7 of 7 for “"Neural radiance field"”.
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Editing Conditional Radiance Fields
A neural radiance field (NeRF) is a scene model supporting high-quality view synthesis, optimized per scene. In this thesis, we explore enabling user editing of a category-level NeRF – also known as a conditional radiance field – trained on a shape category. Specifically, we introduce a method for …
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Generalizable and Efficient Novel View Synthesis with Perceptual Foundations
… our first contribution enhances generalizable Neural Radiance Field (NeRF) architectures through the integration of a Mixture-of-View-Experts (MoE) paradigm. Our proposed model, GNT-MOVE, builds upon recent generalizable NeRF transformer by incorporating expert modules and geometry-aware …
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Trimačio objekto erdvinio modelio sukūrimas iš dvimačių vaizdų naudojant mašininį mokymąsi /
… extracted camera view positions, we employ the Neural Radiance Field method (NeRF) to synthesize novel views and generate a point cloud much more detailed than that obtained by COLMAP. The model used in this study is same to the one presented in the original paper [4] introducing the concept of …
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Addressing Challenges in Object-Based Robot Navigation and Mapping
… challenge is the performance degradation of neural networks when deployed in novel robot operating environments, commonly known as the domain gap problem. Specifically, when a pre-trained 6DoF object pose estimator is used in a novel environment, its pose predictions are often corrupted by …
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Structure from duplicates: Neural inverse rendering from a single image
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Image-based 3D Reconstructions via Differentiable Rendering of Neural Implicit Representations
… advancements in Differentiable Rendering and Neural Implicit Representations have significantly pushed the limits of geometry and appearance reconstruction from RGB images. Utilizing their continuous, differentiable, and less restrictive representations, it is possible to optimize geometry and …
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Learning 3D Robotics Perception using Inductive Priors
Recent advances in deep learning have led to a data-centric intelligence in the last decade, i.e. artificially intelligent models unlocking the potential to ingest a large amount of data and be really good at performing digital tasks such as text-to-image generation, machine-human conversation, and …