paper-with-me

홈 › Papers

Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction

2023-04-21 · Binbin Huang, Xingyue Peng, Siyuan Shen, Suan Xia, Ruiqian Li, Yanhua Yu, Yuehan Wang, Shenghua Gao, Wenzheng Chen, Shiying Li, Jingyi Yu

We introduce Omni-LOS, a neural computational imaging method for conducting holistic shape reconstruction (HSR) of complex objects utilizing a Single-Photon Avalanche Diode (SPAD)-based time-of-flight sensor. As illustrated in Fig. 1, our method enables new capabilities to reconstruct near-$360^\circ$ surrounding geometry of an object from a single scan spot. In such a scenario, traditional line-of-sight (LOS) imaging methods only see the front part of the object and typically fail to recover the occluded back regions. Inspired by recent advances of non-line-of-sight (NLOS) imaging techniques which have demonstrated great power to reconstruct occluded objects, Omni-LOS marries LOS and NLOS together, leveraging their complementary advantages to jointly recover the holistic shape of the object from a single scan position. The core of our method is to put the object nearby diffuse walls and augment the LOS scan in the front view with the NLOS scans from the surrounding walls, which serve as virtual ``mirrors'' to trap lights toward the object. Instead of separately recovering the LOS and NLOS signals, we adopt an implicit neural network to represent the object, analogous to NeRF and NeTF. While transients are measured along straight rays in LOS but over the spherical wavefronts in NLOS, we derive differentiable ray propagation models to simultaneously model both types of transient measurements so that the NLOS reconstruction also takes into account the direct LOS measurements and vice versa. We further develop a proof-of-concept Omni-LOS hardware prototype for real-world validation. Comprehensive experiments on various wall settings demonstrate that Omni-LOS successfully resolves shape ambiguities caused by occlusions, achieves high-fidelity 3D scan quality, and manages to recover objects of various scales and complexity.

📄 PDF Abstract BibTeX arXiv:2304.10780

Code (0)

등록된 구현이 없습니다.

Tasks

NeRFObject

Methods 이 논문이 사용한 방법론

fail 설명 없음

Similar Papers 제목 키워드 기반

OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World

2025-08-05 · Katherine Liu, Sergey Zakharov, Dian Chen, Takuya Ikeda 외 arxiv

We would like to estimate the pose and full shape of an object from a single observation, without assuming known 3D model or category. In this work, we propose OmniShape, the first method of its kind to enable probabilis…

Pose Estimation

OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

2024-05-02 · Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan 외

The advances in vision-language models (VLMs) have led to a growing interest in autonomous driving to leverage their strong reasoning capabilities. However, extending these capabilities from 2D to full 3D understanding i…

Autonomous DrivingcounterfactualCounterfactual ReasoningDecision Making+3

OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

2025-04-06 · CVPR 2025 1 · Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan 외

The advances in vision-language models (VLMs) have led to a growing interest in autonomous driving to leverage their strong reasoning capabilities. However, extending these capabilities from 2D to full 3D understanding i…

Autonomous DrivingcounterfactualCounterfactual ReasoningDecision Making

PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era

2025-09-16 · Xu Zheng, Chenfei Liao, Ziqiao Weng, Kaiyu Lei 외 arxiv

Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional p…

OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging

2026-03-23 · Meilin Liu, Jiaying Wang, Jing Shan arxiv

Federated learning (FL) has become a promising paradigm for collaborative medical image analysis, yet existing frameworks remain tightly coupled to task-specific backbones and are fragile under heterogeneous imaging moda…

Visual Question AnsweringFederated Learning