The paper presents a fully Bayesian framework for reconstructing closed curves from point‑cloud data, treating observed points as noisy perturbations of latent locations constrained to lie on an underlying curve. A non‑parametric prior regularizes the curve, and posterior inference is performed with Markov chain Monte Carlo samplers designed for point‑cloud characteristics. Experiments on synthetic and real LiDAR datasets demonstrate accurate reconstructions and quantified uncertainty over the recovered curves.
LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making reliable point cloud completion challenging with...
Point Diffusion Mamba (PDM) is a new method that fuses diffusion models with state‑space modeling to perform single‑view 3D reconstruction when training data are scarce. It uses a lightweight reconstruction module for unordered point‑clouds, a Local Geometric Aggregation module combined with Mamba blocks to capture both global geometry and local detail, and a Hierarchical Feature Integration Network to merge high‑level semantic and local geometric features for each point. A Dynamic Weighted Sampling strategy further improves reconstruction quality by integrating generative priors, and experiments on ShapeNet and Pix3D show that PDM outperforms existing state‑of‑the‑art approaches.
arXiv:2609.24825v1 Announce Type: new Abstract: LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making...
arXiv:2609.25375v1 Announce Type: cross Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
The paper introduces a fast Bayesian method for estimating homographies from noisy point correspondences, providing a posterior distribution over the homography parameters. A closed‑form solution for the posterior mean in homogeneous coordinates is derived, complemented by an iterative Bayesian approach to address non‑linearities. Experiments on synthetic data and real image stitching show improved accuracy over DLT and supply uncertainty estimates for the homography.
3D scene graphs provide a hierarchical abstraction of environments by encoding spatial entities, such as objects and places, and their relationships. However, existing scene graph systems model object geometry coarsely, relying on partial point clouds or class-level CAD templates, which limits instance-specific shape detail.
arXiv:2609.05744v1 Announce Type: cross Abstract: Reconstructing continuous terrain manifolds from massive, unstructured airborne LiDAR point clouds remains challenging in complex Wildland-Urban Inte...
The paper presents a framework that builds a static point cloud prior map from past camera traversals, augmenting each point with DINOv3 semantic features. During runtime, a local prior patch is retrieved, encoded with a sparse voxel backbone, and fused with lifted multi‑view camera features in bird’s‑eye view. This fused representation is then used by sparse transformer heads to predict 3D objects and vectorized map elements, achieving improved performance on Argoverse 2 without requiring LiDAR for prior‑map construction or online inference.
arXiv:2510. 18999v3 Announce Type: replace-cross Abstract: Reconstructing signed distance functions (SDFs) from point cloud data benefits many robot autonomy capabilities, including localization, mapping, motion planning, and control.
The paper introduces ReconPlusGen, a method that injects a reconstruction prior into multi‑view 3D generation. It predicts a point cloud in canonical space from multiple input images, then deterministically injects this geometry into a diffusion process via noise inversion and modulates the noise to maintain generative flexibility for completing unseen areas and refining visible geometry. Qualitative results show reconstruction on benchmark and real‑world images, along with an illustration of the reconstruction‑guided noise initialization and modulation.
arXiv:2603. 22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy.
ReconPlusGen introduces a method that injects a reconstruction prior into multi‑view 3D generation. By predicting a point cloud in canonical space from multiple input images, the method deterministically injects the geometry into a diffusion process via noise inversion and then modulates the noise to maintain generative flexibility for completing unseen areas and refining visible geometry. The paper presents qualitative results on benchmark and real‑world images, along with an illustration of the reconstruction‑guided noise initialization and modulation.