Technology Category
Computer Vision: Diffusion Models for VisionSearch and Optimization: Mixed Discrete/Continuous SearchKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
Hybrid Latents -- Geometry-Appearance-Aware Surfel Splatting
🤖 AI Summary
This work addresses the reconstruction inaccuracies and rendering inefficiencies in NeRF-like models caused by the tight coupling between geometry and appearance. To this end, we propose a hybrid Gaussian–hash-grid radiance representation that explicitly decouples low-frequency geometry from high-frequency texture through frequency-aware embedding. Each Gaussian is augmented with a latent feature vector, jointly optimized alongside hash-grid features. The method further incorporates hard opacity decay, a binary cross-entropy opacity loss, and a probabilistic pruning strategy to automatically sparsify and refine the Gaussian primitives. Experiments demonstrate that our approach achieves superior reconstruction fidelity and rendering efficiency on both synthetic and real-world datasets, using an order of magnitude fewer Gaussians than existing methods.
Technology Category
Application Category
📝 Abstract
We introduce a hybrid Gaussian-hash-grid radiance representation for reconstructing 2D Gaussian scene models from multi-view images. Similar to NeST splatting, our approach reduces the entanglement between geometry and appearance common in NeRF-based models, but adds per-Gaussian latent features alongside hash-grid features to bias the optimizer toward a separation of low- and high-frequency scene components. This explicit frequency-based decomposition reduces the tendency of high-frequency texture to compensate for geometric errors. Encouraging Gaussians with hard opacity falloffs further strengthens the separation between geometry and appearance, improving both geometry reconstruction and rendering efficiency. Finally, probabilistic pruning combined with a sparsity-inducing BCE opacity loss allows redundant Gaussians to be turned off, yielding a minimal set of Gaussians sufficient to represent the scene. Using both synthetic and real-world datasets, we compare against the state of the art in Gaussian-based novel-view synthesis and demonstrate superior reconstruction fidelity with an order of magnitude fewer primitives.
Problem
Research questions and friction points this paper is trying to address.
geometry-appearance entanglement
high-frequency texture compensation
geometric error
scene reconstruction
novel-view synthesis
Innovation
Methods, ideas, or system contributions that make the work stand out.
hybrid latent representation
geometry-appearance disentanglement
frequency-based decomposition
probabilistic pruning
Gaussian splatting
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