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:2607. 26645v1 Announce Type: cross Abstract: Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion.
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.
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:2609.10322v1 Announce Type: new Abstract: Transferring the rich priors of large 2D foundation models to sparse 3D LiDAR remains challenging, as training native 3D foundation models at comparabl...
arXiv:2404.09431v3 Announce Type: replace Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...
PatchScene is a diffusion-based framework that completes large-scale LiDAR scenes using a patch-based voxel diffusion paradigm, generating fine-grained geometry in localized 3D regions. It incorporates a confidence-guided spatio-temporal fusion mechanism to merge overlapping patches and adjacent frames, and introduces an Annular-Flow diffusion strategy that propagates high-fidelity information from near to far ranges. Experiments on SemanticKITTI show state-of-the-art performance, with the model trained on 20 m LiDAR ranges generalizing to 50 m scenes without retraining.
arXiv:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
eSharp is a monocular depth estimation framework that uses an event camera and pretrained diffusion priors to produce accurate, sharp metric depth from sparse LiDAR ground truth. It tackles challenges of fast motion and high-dynamic-range scenes by cross‑modal distillation, generating dense pseudo‑labels from a co‑registered frame camera. Experiments demonstrate that eSharp surpasses existing event‑to‑depth baselines in both depth accuracy and spatial fidelity, positioning diffusion models as a strong foundation for event‑driven 3D perception.
SplatLabel is an automated pipeline that uses a 4D Gaussian representation to generate LiDAR segmentation and semantic occupancy grids with predictive confidence. It models dynamic scenes through an explicit temporal manifold, tracking moving actors without requiring pre‑annotated 3D bounding boxes. By integrating 360‑degree LiDAR depth maps and distilling soft probabilities from 2D models, it resolves semantic ambiguities over time and space, and evaluates pseudo‑labels via a selective classification framework that balances precision and recall.
arXiv:2606. 20189v1 Announce Type: cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).
arXiv:2606. 20189v3 Announce Type: replace-cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).