Papers for

3d scanning professionals

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Interactive 3d reconstruction improves with sparse user clicks

Click3R: Interactive Stereo 3D Reconstruction with Sparse Correspondence Clicks

Abstract: Despite impressive progress, existing feed-forward large 3D reconstruction models produce a single, irrevocable prediction. When these models fail, there is no mechanism to correct their mistakes. To address this limitation, we introduce Click3R, a framework for interactive geometric correction of feed-forward models using sparse human correspondence clicks. Given an image pair and a small set of cross-view point correspondences provided by a user, Click3R injects geometric constraints into the 3D reconstruction networks through a lightweight point correspondence adapter, enabling the model to resolve ambiguities in challenging scenarios such as repetitive textures, symmetric structures, large viewpoint changes, or textureless regions. To evaluate interactive reconstruction, we curate a challenging benchmark specifically designed to expose failure modes of existing feed-forward 3D reconstruction methods. Experiments show that Click3R reduces reconstruction error with as few as a single correspondence click, improving performance on both standard and our curated benchmarks. Our results highlight that sparse human interaction provides an effective and practical mechanism for correcting geometric reconstruction errors.

Mon 9 NovComputer Vision and Pattern Recognition

The gist

3D reconstruction models usually make one guess about the shape of objects from images and cannot fix themselves if wrong. This paper presents Click3R, a method where a person can click on a few matching points between two images to help the model improve its 3D guess. These clicks add helpful geometric clues that fix tricky cases like repeated patterns or when parts of objects look very similar. The authors show that even one click can significantly reduce errors in 3D reconstructions.

Open → 2611.12875v1

Hierarchical method improves real time 3d scene reconstruction quality and speed

Hierarchical Incremental Beta Splatting for Online Reconstruction

Abstract: Online 3D Gaussian Splatting reconstructs a scene during capture: each keyframe spawns primitives once and refines them for only a few dozen iterations, leaving little chance to fix poor initialization or missing detail. Gaussians are also inherently soft, needing many overlaps for solid surfaces. We present Hierarchical Incremental Beta Splatting (HIBS), which brings beta kernels to incremental reconstruction. Their shape parameter interpolates between a soft, Gaussian-like profile and a flat-topped, near-opaque surfel, acting as a confidence dial for geometry. Our confidence-aware spawning uses geometric observability and multi-view depth agreement to initialize both opacity and shape, producing solid primitives where geometry is verifiable and soft, correctable primitives where it is not. As online densification, error-driven relocation recycles stale primitives into photometric-error hotspots. Bounded beta kernel support enables exact tile culling and faster rasterization, and a closed-form, shape-preserving merging formulation provides a level-of-detail (LOD) hierarchy for fast optimization and real-time rendering. On Mip-NeRF 360 in the on-the-fly protocol, HIBS improves PSNR by up to 0.8 dB while reducing optimization time by up to 40%. Project page: https://linus-franke.com/hibs/

Thu 5 NovComputer Vision and Pattern RecognitionGraphics

The gist

Making 3D pictures of a scene while you capture it is tricky because early guesses about the shapes might be wrong and not fixed later. The authors provide a method that changes how parts of the 3D scene are created and adjusted, using a special shape that can be soft or solid depending on how confident the system is about the geometry. This approach helps keep solid parts where the shape is clear and soft parts where it's uncertain, and it recycles less useful parts to focus on detailed areas. Their method also speeds up the process and improves the quality of the 3D images as measured on a test set.

Open → 2611.08288v1