Dens3R: A Foundation Model for 3D Geometry Prediction
Authors: name: Xianze Fang, name: Jingnan Gao, name: Zhe Wang, name: Zhuo Chen, name: Xingyu Ren, name: Jiangjing Lyu, name: Qiaomu Ren, name: Zhonglei Yang, name: Xiaokang Yang, name: Yichao Yan, name: Chengfei Lyu

Recent advances in dense 3D reconstruction have led to significant progress,
yet achieving accurate unified geometric prediction remains a major challenge.
Most existing methods are limited to predicting a single geometry quantity from
input images. However, geometric quantities such as depth, surface normals, and
point maps are inherently correlated, and estimating them in isolation often
fails to ensure consistency, thereby limiting both accuracy and practical
applicability. This motivates us to explore a unified framework that explicitly
models the structural coupling among different geometric properties to enable
joint regression. In this paper, we present Dens3R, a 3D foundation model
designed for joint geometric dense prediction and adaptable to a wide range of
downstream tasks. Dens3R adopts a two-stage training framework to progressively
build a pointmap representation that is both generalizable and intrinsically
invariant. Specifically, we design a lightweight shared encoder-decoder
backbone and introduce position-interpolated rotary positional encoding to
maintain expressive power while enhancing robustness to high-resolution inputs.
By integrating image-pair matching features with intrinsic invariance modeling,
Dens3R accurately regresses multiple geometric quantities such as surface
normals and depth, achieving consistent geometry perception from single-view to
multi-view inputs. Additionally, we propose a post-processing pipeline that
supports geometrically consistent multi-view inference. Extensive experiments
demonstrate the superior performance of Dens3R across various dense 3D
prediction tasks and highlight its potential for broader applications.