This work presents GeoSVR, an explicit voxel-based framework that explores and extends the under-investigated potential of sparse voxels to achieve accurate, detailed, and complete surface reconstruction with high efficiency.

A large-scale egocentric dataset with GT 3D and lighting variation for benchmarking NVS and visual relocalization.

Feed-forward 3D reconstruction with flexibility to rig constraints

We trained MeshLLM, an Object-to-Code inference framework that generates Blender Python scripts to reconstruct 3D meshes from point clouds in a structured and editable manner.

OnlineSplatter enables pose-free 3D reconstruction of free-moving objects from monocular video by fusing frame-wise features into a compact Gaussian field via a dual-key memory, with constant-time and memory efficiency.

3D reconstruction of hand considering consistency from constrained resolution

We present a practical system that reconstructs both line-of-sight (LOS) and non-line-of-sight (NLOS) 3D scenes using a single mmWave radar.

We propose Puzzles, a controllable augmentation that simulates high-quality posed video-depth data from a single image or clip, boosting 3D reconstruction performance with much less training data.

InFlux is the first real-world benchmark that provides per-frame ground truth camera intrinsics for videos with dynamic intrinsics, and current baselines struggle to predict accurate intrinsics on our benchmark.

We present PLANA3R, a zero-shot method for metric Planar 3D Reconstruction from unposed two-view images.

SIU3R: the first alignment-free framework for generalizable simultaneous understanding and 3D reconstruction from unposed images.

This paper presents SpatialLM, a large language model designed to process 3D point cloud data and generate structured scene descriptions.

We develop the first HDR 3D measurement framework for event-based structured light systems, enabling accurate 3D reconstruction even under extreme conditions.

Use ground-based cameras to retrieve physical properties of clouds at a high spatial and temporal resolution.

We propose CDC-GS, a 3DGS densification strategy based on visual prior which is perpendicular to existing gradient-based ones. Our method improves the novel view synthesis quality by a large margin without increasing the number of Gaussians.