1 citations · 1 across the 3 of their papers we have counts for
7 papers
ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning
Ken Deng, Jiarui Liu, Guibiao Liao +7
We present ScenePlacer, a framework for scene-level pose estimation of objects generated by state-of-the-art native shape generation models, placing generated 3D objects into scene…
DistScene: Object-to-Scene Distillation for 3D Scene Generation
Kunming Luo, Hongyu Yan, Ken Deng +6
We present DistScene, a framework for single-image compositional 3D scene generation by jointly modeling the environment and individual objects. Unlike existing methods that repres…
SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images
Guibiao Liao, Mochu Xiang, Heng Li +6
Compositional 3D scene generation aims to recover complete 3D object shapes and their spatial arrangement from visual observations. Recent image-conditioned 3D generators provide s…
Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation
Ken Deng, Yifu Qiu, Yoni Kasten +2
We study whether vision-language models (VLMs) can solve relative camera pose estimation (RCPE) from image pairs, a direct test of multi-view spatial reasoning. We cast RCPE as a d…
GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation
Ken Deng, Yunhan Yang, Jingxiang Sun +4
We introduce GeoSAM2, a prompt-controllable framework for 3D part segmentation that casts the task as multi-view 2D mask prediction. Given a textureless object, we render normal an…
DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flow
Ken Deng, Yuan-Chen Guo, Jingxiang Sun +6
Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present Detai…
NeRF-VPT: Learning Novel View Representations with Neural Radiance Fields via View Prompt Tuning
Linsheng Chen, Guangrun Wang, Liuchun Yuan +3
Neural Radiance Fields (NeRF) have garnered remarkable success in novel view synthesis. Nonetheless, the task of generating high-quality images for novel views persists as a critic…