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Volume Rendering Digest (for NeRF)
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Neural Radiance Fields employ simple volume rendering as a way to overcome the challenges of differentiating through ray-triangle intersections by leveraging a probabilistic notion of visibility. This is achieved by assuming the scene is composed by a cloud of light-emitting particles whose density changes in space. This technical report summarizes the derivations for differentiable volume rendering. It is a condensed version of previous reports, but rewritten in the context of NeRF, and adopting its commonly used notation.
Forward citations
Cited by 5 Pith papers
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Normal-NeRF: Ambiguity-Robust Normal Estimation for Highly Reflective Scenes
Normal-NeRF uses transmittance gradients and dual density activations to reconstruct and render shiny scenes more accurately than prior NeRF-based methods.
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Triangle Splatting for Real-Time Radiance Field Rendering
A triangle-soup representation with a compact normalized window function is optimized end-to-end and beats Gaussian and convex splatting baselines on LPIPS while rendering at real-time rates.
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CATSplat: Context-Aware Transformer with Spatial Guidance for Generalizable 3D Gaussian Splatting from A Single-View Image
CATSplat uses image captions and back-projected depth points to improve single-image 3D Gaussian scene reconstruction and novel view synthesis.
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Multi-Modal Neural Radio Radiance Field for Localized Statistical Channel Modelling
MM-LSCM fuses RSRP and LiDAR point clouds in a NeRF-style volume renderer to predict angular power spectra in unexplored areas, outperforming WNOMP and a LiDAR-free ablation on a lab dataset.
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R3GS: Gaussian Splatting for Robust Reconstruction and Relocalization in Unconstrained Image Collections
R3GS integrates appearance-conditioned hash features, a fine-tuned human-detector visibility map, and a fixed sky sphere into 3D Gaussian Splatting to improve novel view synthesis and relocalization on Phototourism scenes.
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