The paper introduces the Measurement-Adaptive Light-Cone Transform (MALCT), a physics-guided framework that maintains the efficient Fourier-domain inversion of traditional light-cone transform methods while adapting to real-world measurement noise and deviations in non-line-of-sight imaging. MALCT integrates an analytical response with a learned response via measurement-conditioned weighting and predicts a frequency-dependent regularization map within a unified variational formulation. An attention-enhanced spatio-temporal feature extraction module captures long-range temporal dependencies and local spatial correlations, enabling robust reconstruction under low-signal conditions and with reduced acquisition time, as demonstrated on synthetic data and real measurements from various NLOS systems.
By Lianfang Wang, Xueying Liu, Junnan Dong, Qinghua Zhang, Jun Liu, Yong Wang, Yuping Duan
The paper introduces a state‑aware framework that reconstructs both foreground and hidden scenes from single‑photon LiDAR histograms affected by partially transmissive occluders. It classifies each ray into no‑return, single‑return, or dual‑return states, guiding a two‑head neural field that jointly refines waveform reconstruction and geometry localization. A new paired dataset of occluded and clean LiDAR captures validates the method, showing improved depth and point‑cloud accuracy over existing baselines.
By Ziting Wen, Runrong Deng, Zili Zhang, Haitao Zheng, Yuecong Xu, Xiaoqiang Ren, Guodong Shi, Kemi Ding
The paper presents memory‑efficient GPU pipelines that accelerate real‑time non‑line‑of‑sight (NLOS) reconstruction. By redesigning two wave‑based algorithms—f‑k migration and phasor‑fields—with fused kernels, warp‑level photon binning, batched transforms, CUDA graph replay, and selective FP16 storage, the authors achieve up to 42× speed‑ups over a reference streaming pipeline and 14× over the fastest published GPU baseline while reducing memory usage to as little as 2.5%. The work also includes an ablation study of implementation choices and introduces three denoising strategies that leverage the increased frame budget for future NLOS video processing.
By Alfonso L\'opez-Ruiz, Diego Royo
Projector-camera (ProCams) systems achieve active scene perception and controllable appearance manipulation via structured illumination, serving as a core infrastructure for spatial augmented reality, projection mapping, and surface reflectance acquisition. Existing inverse-rendering methods for ProCams deliver high-fidelity results but rely on time-consuming per-scene optimization, while mainstream feed-forward 3D reconstruction models produce baked appearance that cannot adapt to spatially varying projector illumination.
arXiv:2608.20602v1 Announce Type: cross
Abstract: Many consumer smartphones, stereo cameras, and light field cameras record multiple synchronized viewpoints in a single exposure event. However, novel...
By Shamus Li, Ruiming Cao, Laura Waller, Kristina Monakhova, Sara Fridovich-Keil
arXiv:2609.22479v1 Announce Type: new
Abstract: We address the problem of recovering high-speed videos from dynamic scenes under extreme photon sparsity. Existing methods rely on aggregating photon d...
By Jerry Yan, Matteo Forlivesi, Bowen Tan, Andrew Xie, Siddharth Somasundaram, Sotiris Nousias
arXiv:2607.24495v2 Announce Type: replace
Abstract: Structured-light (SL) cameras power depth sensing in millions of devices, and recent neural SL decoding methods have substantially improved their d...
By Jiaheng Li, Binsheng Zhang, Xinhai Chang, Wenzheng Chen
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
arXiv:2609.36844v1 Announce Type: new
Abstract: Transparent surfaces are ubiquitous in built environments, yet they remain a persistent failure case for robotic perception. RGB cameras perceive the b...
By Suhani Grover, Astik Srivastava, Viswas Dinesh, Avinash Sharma, K. Madhava Krishna
GLOW is a Global Illumination‑aware inverse rendering framework for indoor scenes captured with dynamic co‑located light and camera setups. It combines a neural implicit surface representation with a neural radiance cache to jointly optimize geometry and reflectance, while introducing a dynamic radiance cache and a surface‑angle‑weighted radiometric loss to handle near‑field motion, strong inter‑reflections, and specular highlights. Experiments demonstrate that GLOW significantly outperforms prior methods in estimating material reflectance under both natural and co‑located illumination.
By Jiaye Wu, Saeed Hadadan, Geng Lin, Peihan Tu, Auguste Gezalyan, Matthias Zwicker, David Jacobs, Roni Sengupta
DynGhost is a transformer-based architecture designed for dynamic ghost imaging, addressing two key limitations of prior methods: the lack of temporal coherence exploitation and the assumption of Gaussian noise. It alternates spatial and temporal attention blocks and incorporates a quantum-aware training framework that uses realistic detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization to handle Poissonian noise. Experiments on multiple benchmarks show that DynGhost surpasses traditional reconstruction methods and existing deep learning models, especially in dynamic and photon-starved scenarios.
By Vittorio Palladino, Ahmet Enis Cetin
arXiv:2609.28439v1 Announce Type: new
Abstract: The adoption of CMOS sensors in mobile photography is frequently compromised by the rolling shutter (RS) effect, which introduces geometric distortions...
By Xiang Ji, Guixu Lin, Jiancheng Zhao, Zhengwei Yin, Yinqiang Zheng