3d-reconstruction

Surflo is a feed-forward 3D reconstruction model that compresses unposed RGB views into latent tokens and decodes consistent 3D surface points via flow matching, enabling variable-resolution output and outperforming existing methods in speed.

BA-T is an iterative Transformer architecture for two-view bundle adjustment that improves 3D reconstruction accuracy and cross-view consistency using a lightweight design with only 16% of conventional decoder parameters, matching or surpassing larger models.

This paper introduces SEIG, a framework that uses pretrained vision-language models to reconstruct 3D scenes from single images as editable Blender programs through progressive refinement of geometry, materials, composition, and lighting.

SurGe introduces a Neighborhood Attention Decoder and a reformulated scale-invariant gradient matching loss to improve local surface geometry accuracy in feedforward 3D reconstruction, particularly for thin structures. It achieves state-of-the-art average rank on zero-shot monocular geometry benchmarks, with better local point map and normal metrics.

4D Gaussian Splatting is a technique that converts flat 2D images into three-dimensional spatial data, enabling reconstruction of different angles from live footage.

Introduces GARD, a diffusion-based framework that operates in the feature space of a feed-forward 3D reconstructor to jointly recover scene geometry and high-quality imagery from degraded inputs.

TriSplat is a feed-forward 3D reconstruction network that uses oriented triangle primitives to directly generate simulation-ready meshes from single images, bypassing expensive post-processing steps. It achieves geometry-faithful reconstructions while maintaining competitive novel-view rendering quality.

HorizonStream introduces a long-horizon attention mechanism for streaming 3D reconstruction that explicitly models geometric propagation via an evidence influence kernel, achieving stable, scalable reconstruction with constant memory and linear time complexity, and generalizing to sequences over 10,000 frames.

GenRecon introduces a method for 3D scene reconstruction that integrates generative 3D priors with multi-view image conditioning, achieving high-fidelity, editable mesh reconstructions of indoor environments and outperforming existing methods by 16%.

Presents SceneAligner, a deep learning approach for floorplan localization that uses 3D scene reconstruction and cross-modal correspondence learning to work in real-world environments with limited data.

UniT is a unified feed-forward model for geometry perception using a Group Autoregressive Transformer that integrates multiple paradigms (online/offline, multi-modal, long-horizon) while maintaining metric-scale accuracy via scale-adaptive loss and queue-style KV caching. It achieves state-of-the-art performance on ten benchmarks spanning seven tasks.

A Gaussian splat of a strawberry, created from 90 perspectives with focus stacking, using slang-splat for training and SuperSplat for viewing.

Archaeologists used CT scans and 3D reconstruction to identify a Pompeii victim as a likely Roman doctor.

EgoForce is a monocular 3D hand reconstruction framework that uses a unified network with differentiable forearm representation, arm-hand transformers, and ray space solvers to recover absolute hand pose and position across different camera models, achieving state-of-the-art accuracy on egocentric benchmarks.

Lite3R is a model-agnostic framework that improves the efficiency of transformer-based 3D reconstruction using sparse linear attention and FP8-aware quantization. It reduces latency and memory usage by up to 2.4x while maintaining geometric accuracy on backbones like VGGT and DA3-Large.

MoCam is a research paper introducing a diffusion-based framework for unified novel view synthesis that dynamically coordinates geometric and appearance priors to improve robustness against geometric errors.

VidSplat is a training-free generative reconstruction framework that uses video diffusion priors to recover complete 3D scenes from sparse inputs by synthesizing novel views.

An individual has created a tool using Claude Code to generate true interactive 3D property tours from existing image captures, aiming to replace expensive hardware and subscriptions like Matterport.

This paper introduces TT4D, a novel pipeline and large-scale dataset for reconstructing table tennis gameplay in 4D from monocular videos. It features a unique lift-first approach that estimates 3D ball trajectories and spin before time segmentation, enabling robust reconstruction even with occlusions.

AnyRecon proposes a scalable framework for 3D reconstruction from arbitrary sparse inputs using a video diffusion model with persistent scene memory and geometry-aware conditioning.