arXiv:2609.36929v1 Announce Type: new
Abstract: Recent event-based depth estimation methods successfully transfer geometric priors from vision foundation models via cross-modal distillation. However,...
By Thai Duy Nguyen, Addison Lin Wang
The paper introduces HypoDepth, an event-image monocular depth estimation framework that uses a discrete Depth Hypothesis Volume (DHV) to convert depth regression into a constrained search problem. By building a lightweight 3D cost volume between DHV features and contextual features, the method performs multi-scale correlation search for stable residual optimization, enabling efficient global-to-local refinement across resolutions. Experiments on DSEC and MVSEC show state‑of‑the‑art performance, strong zero‑shot generalization, and real‑time capability on resource‑limited devices.
By Daikun Liu, Teng Wang, Changyin Sun
The paper introduces a depth completion method that focuses on extrapolating depth beyond LiDAR coverage, a scenario often overlooked in current research. It uses a frozen monocular depth foundation model, aligns it with sparse LiDAR points, and refines the result with a module that fuses the prior and sparse anchors to produce dense metric depth. A novel high‑frequency normal‑consistency loss and a new evaluation protocol that separates LiDAR‑supported and beyond‑coverage regions are also presented, demonstrating strong accuracy in unobserved areas while maintaining performance where LiDAR data exists.
By Maximilian Jansen, Gasser Elazab, Praveen Kulkarni
FlowMoDE introduces a coarse‑to‑fine flow‑matching framework for monocular depth estimation that balances efficiency and detail reconstruction. The method first decodes coarse depth conditioned on diffused features from a pre‑trained semantic encoder, then refines fine structural patterns with expanded decoding layers. Trained only on a synthetic indoor dataset, FlowMoDE achieves a 14.1% lower relative error and a 9.9% lower Chamfer distance compared to state‑of‑the‑art methods across five real‑world indoor and outdoor datasets, and demonstrates strong generalization to realistic robotic scenes.
By Liangjing Shao, Wanhao Liu, Jinsong Lin, Zhiwei Fang, Hongliang Ren
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.
By Qingdong Xu, Jiajun Zhu, Shilin Zhu, Xinjing He, Chao Lu, Huanran Wang, Jiyao Zhang
arXiv:2505. 08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes.
By Chuanzhi Xu, Haoxian Zhou, Langyi Chen, Haodong Chen, Zeke Zexi Hu, Zhicheng Lu, Ying Zhou, Vera Chung, Qiang Qu, Weidong Cai