Accurate surface reconstruction from unposed images is crucial for efficient 3D object or scene creation. However, it remains challenging, particularly for the joint camera pose estimation. Previous approaches have achieved impressive pose-free surface reconstruction results in dense-view settings, but could easily fail for sparse-view scenarios without sufficient visual overlap. In this paper, we propose a new technique for pose-free surface reconstruction, which follows triplane-based signed distance field (SDF) learning but regularizes the learning by explicit points sampled from ray-based diffusion of camera pose estimation. Our key contribution is a novel Geometric Consistent Ray Diffusion model (GCRayDiffusion), where we represent camera poses as neural bundle rays and regress the distribution of noisy rays via a diffusion model. More importantly, we further condition the denoising process of RGRayDiffusion using the triplane-based SDF of the entire scene, which provides effective 3D consistent regularization to achieve multi-view consistent camera pose estimation. Finally, we incorporate RGRayDiffusion into the triplane-based SDF learning by introducing on-surface geometric regularization from the sampling points of the neural bundle rays, which leads to highly accurate pose-free surface reconstruction results even for sparse-view inputs. Extensive evaluations on public datasets show that our GCRayDiffusion achieves more accurate camera pose estimation than previous approaches, with geometrically more consistent surface reconstruction results, especially given sparse-view inputs.

Dynamic depth estimation continues to be crucial but challenging mainly due to the violation of multi-view consistency raised by dynamic areas. Recent approaches have made impressive progress by implicitly fusing the intra-relation features, but is still limited for heterogeneous dynamic scenes. In this paper, we propose a new intra-relation feature fusion, which can significantly improve the fusion quality using an explicit regularization from 3D scene flow cues. We first introduces a Dual Cross-Cue Fusion (D-CCF) module for depth prediction, and further build up an efficient 3D scene flow estimation as explicit 3D spatio-temporal corresponding priors to regularize the depth prediction. Finally, by jointly learning both the depth prediction and 3D scene flow estimation in a unsupervised manner, we achieve more accurate dynamic depth estimation towards spatio-temporal consistency. By extensive evaluation on challenging benchmarks (KITTI and DDAD), our approach can achieve better depth estimation results than state-of-the-art approaches in both static and dynamic areas, which especially maintains the spatio-temporal consistency for dynamic scenes.