3D Gaussian Splatting has recently revolutionised novel viewsynthesis as well as many other 3D vision methods and applications.Drawing inspiration from this representation, we now rethink texturesto overcome the main issues of UV mapping while considerably lower-ing their memory footprint. Heat Kernel Textures (HKTex) eliminateUV unwrapping as well as their persistent issues of wasted UV space,seams, distortions, vertex-duplication, and varying resolution. Groundedin discrete Riemannian geometry and intrinsically defined on any man-ifold surface discretised as a triangular mesh, HKTex uses anisotropicheat kernels as geodesic equivalents to Gaussians. Like our kernels, alsothe optimisation of their position and the adaptive densification strate-gies were redefined to operate on the surface of the object to be tex-tureised. Our novel representation is also fully integrated with a physi-cally based renderer and can be optimised either from existing texturesor multi-view images. Our project page and code are available at circle-group.github.io/research/HeatKernelTextures.

3D Gaussian Splatting has recently revolutionised novel viewsynthesis as well as many other 3D vision methods and applications.Drawing inspiration from this representation, we now rethink texturesto overcome the main issues of UV mapping while considerably lower-ing their memory footprint. Heat Kernel Textures (HKTex) eliminateUV unwrapping as well as their persistent issues of wasted UV space,seams, distortions, vertex-duplication, and varying resolution. Groundedin discrete Riemannian geometry and intrinsically defined on any man-ifold surface discretised as a triangular mesh, HKTex uses anisotropicheat kernels as geodesic equivalents to Gaussians. Like our kernels, alsothe optimisation of their position and the adaptive densification strate-gies were redefined to operate on the surface of the object to be tex-tureised. Our novel representation is also fully integrated with a physi-cally based renderer and can be optimised either from existing texturesor multi-view images. Our project page and code are available at circle-group.github.io/research/HeatKernelTextures.

We introduce the Large Sparse Reconstruction Model tostudy how scaling transformer context windows affects feed-forward 3Dreconstruction. Although recent object-centric feed-forward methods pro-duce robust, high-quality reconstructions, they still lag behind dense-view optimization in recovering fine-grained texture and appearance. Weshow that expanding the context window—by substantially increasingthe number of active object and image tokens—narrows this gap andenables high-fidelity 3D object reconstruction and inverse rendering. Toscale effectively, we adapt native sparse attention [68] for 3D reconstruc-tion with three key contributions: (1) an efficient coarse-to-fine pipelinethat focuses computation on informative regions by predicting sparsehigh-resolution residuals; (2) a 3D-aware spatial routing mechanism thatestablishes accurate 2D-3D correspondences using explicit geometric dis-tances rather than standard attention scores; and (3) a custom block-aware sequence-parallel strategy with an All-gather-KV protocol to bal-ance dynamic, sparse workloads across GPUs. As a result, LSRM handles20× more object tokens and >2× more image tokens than prior state-of-the-art (SOTA) methods. Extensive evaluations on standard …

Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes perpixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23–26% on synthetic data and 6–16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at …

Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes perpixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23–26% on synthetic data and 6–16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at …

We introduce the Large Sparse Reconstruction Model tostudy how scaling transformer context windows affects feed-forward 3Dreconstruction. Although recent object-centric feed-forward methods pro-duce robust, high-quality reconstructions, they still lag behind dense-view optimization in recovering fine-grained texture and appearance. Weshow that expanding the context window—by substantially increasingthe number of active object and image tokens—narrows this gap andenables high-fidelity 3D object reconstruction and inverse rendering. Toscale effectively, we adapt native sparse attention [68] for 3D reconstruc-tion with three key contributions: (1) an efficient coarse-to-fine pipelinethat focuses computation on informative regions by predicting sparsehigh-resolution residuals; (2) a 3D-aware spatial routing mechanism thatestablishes accurate 2D-3D correspondences using explicit geometric dis-tances rather than standard attention scores; and (3) a custom block-aware sequence-parallel strategy with an All-gather-KV protocol to bal-ance dynamic, sparse workloads across GPUs. As a result, LSRM handles20× more object tokens and >2× more image tokens than prior state-of-the-art (SOTA) methods. Extensive evaluations on standard …