PhaseSurf introduces a neural volume rendering framework that uses Metric-Phase Fields (MPFs) to handle thin structures and sign-ambiguous geometry. MPFs combine a nonnegative metric field for unsigned proximity with a continuous phase field that softly indicates sign, producing a signed composite implicit function with stable gradients. The method includes specialized training losses for metric consistency and phase transition stability, enabling robust iso-surface extraction and outperforming both SDF- and UDF-based approaches on multi-view datasets with challenging geometry.
arXiv:2605.10360v3 Announce Type: replace Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains...
arXiv:2605.26616v2 Announce Type: replace Abstract: While 3D Gaussian Splatting has achieved remarkable success in photorealistic novel view synthesis, its pursuit of fast and high-fidelity 3D recons...
M-plicits introduces a multiscale framework for neural implicit surfaces that models a surface as a residual sum of MLPs trained on nested neighborhoods. By localizing supervision to narrow bands around previous zero-level sets, the method achieves robustness to noisy input, avoids costly mesh extraction, and enables a multiscale sphere-tracing algorithm with analytical normal computation. Experiments on Stanford and Thingi32 show superior Chamfer distance and IoU metrics compared to existing methods while using far fewer parameters.
The paper introduces SVRecon, a generalizable neural surface reconstruction framework that uses sparse volumetric representations to achieve high-resolution 3D reconstruction. It employs a two-stage architecture: first predicting occupied voxels with an occupancy network, then rendering only within those regions using specialized sparse algorithms. This approach allows reconstruction at resolutions up to 512³ on 32 GB hardware, producing smoother and more precise surfaces, especially in sparse-view scenarios.
arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.
arXiv:2610.19681v1 Announce Type: new Abstract: Accurate 3D reconstruction in real-world environments remains a significant challenge due to the coexistence of reflective and non-reflective surfaces,...
TetraSDF is an analytic isosurface extraction framework that represents a neural signed distance function (SDF) using a ReLU MLP combined with a multi-resolution tetrahedral positional encoder. This design preserves a global continuous piecewise affine (CPWA) structure, enabling exact extraction of the zero-level set as a triangle mesh while maintaining the expressiveness of grid-based encoders. The method also introduces a fixed analytic input preconditioner to reduce directional bias and stabilize training, achieving SDF reconstruction accuracy that matches or surpasses existing grid-based encoders across multiple benchmarks.
arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.
Implicit Neural Representations (INRs) have become the standard for continuous 2D shape modeling, but they suffer from black-box uneditability, vulnerability to noise, and high parameter counts that severely hinder deployment on edge devices. We introduce Fluid-SDF, a highly compressed, differentiable Constructive Solid Geometry (CSG) framework that models shapes using explicit geometric primitives blended via a smooth minimum function.
Accurate 3D reconstruction in real-world environments remains a significant challenge due to the coexistence of reflective and non-reflective surfaces, which pose distinct modeling demands. Existing m...
NeuSOGA3D is a hybrid neuro‑symbolic framework that reconstructs 3D geometry from unorganized point clouds by combining learned perceptual priors with explicit symbolic geometric reasoning. It projects point clouds onto orthographic planes, builds symbolic implicit spline representations, and fuses them via shape‑preserving constructive solid geometry to produce a coarse visual hull. Additional detail is added through cross‑sectional decomposition and volumetric reconstruction with Partial Shape‑Preserving Splines, yielding CAD‑compatible, structurally meaningful models across all 40 ModelNet40 categories.
NeuDonatello is a new framework for neural signed distance function (SDF) learning that explicitly models spatially varying uncertainty using Monte Carlo sampling. By incorporating this uncertainty into an adaptive regularization scheme and an uncertainty-aware SDF-to-density conversion, the method selectively strengthens geometric constraints where RGB supervision is unreliable, thereby improving surface reconstruction accuracy. Experiments show that NeuDonatello achieves state‑of‑the‑art results on diverse scenes using only posed RGB images.