activity
20222026
most citedNative and Compact Structured Latents for 3D Generation

4 citations · 6 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV2026

OctWorld: Long-Range World-Consistent Video Generation with Octree-Based 3D Mapping

Zelong Lv, Sicheng Xu, Jianfeng Xiang +5

We present OctWorld, a video diffusion framework with persistent 3D memory for generating explorable, world-consistent, and high-fidelity visual scenes. Given a single image, OctWo…

cs.CV2026

Dense Metric Depth Completion from Sparse Direct Time-of-Flight Sensors

Hakyeong Kim, Ruicheng Wang, Chengtang Yao +2

Direct Time-of-Flight (dToF) sensors provide highly accurate metric depth and are more robust than indirect ToF systems in challenging real-world conditions. However, their high ma…

cs.CV2026

MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement

Lingyu Kong, Ruicheng Li, Ruicheng Wang +4

Monocular geometry estimation has recently achieved impressive performance across diverse scenes. However, state-of-the-art models still face notable distortion in local 3D structu…

cs.CV2026

Stabilizing Streaming Video Geometry via Dynamic Feature Normalization

Xiaoyang Lyu, Muxin Liu, Xiaoshan Wu +5

Consistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving, embodied AI, and large-scale reconstruction. While mod…

cs.CV20254 cited

Native and Compact Structured Latents for 3D Generation

Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu +8

Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to cap…

cs.CV2025

MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

Ruicheng Wang, Sicheng Xu, Yue Dong +6

We propose MoGe-2, an advanced open-domain geometry estimation model that recovers a metric scale 3D point map of a scene from a single image. Our method builds upon the recent mon…