Single-photon avalanche diode (SPAD) cameras produce binary images where each pixel records zero or one photon arrival, making scene reconstruction a fundamentally one-to-many inverse problem. As more binary frames are accumulated, the uncertainty diminishes, transitioning from stochastic to near-deterministic scene understanding. The study introduces a conditional generative framework using a Hypergeometric frame‑thinning process to model this ambiguity and evaluates its impact on tasks such as character recognition, QR code decoding, and facial analysis.
By Haejoon Lee (Carnegie Mellon University), Mohit Gupta (University of Wisconsin-Madison), Vijayakumar Bhagavatula (Carnegie Mellon University), Aswin C. Sankaranarayanan (Carnegie Mellon University)
Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brightness-change observations with high temporal resolution, but prior works often treat voxel channels as an unordered or static feature stack before fusion, rather than explicitly modeling their within-window temporal evolution, weakening the temporal evidence that makes events useful.
arXiv:2603.14023v3 Announce Type: replace
Abstract: This work introduces and demonstrates the first system capable of imaging fast-moving extended non-rigid objects through strong atmospheric turbule...
By Yu-Hsiang Huang, Levi Burner, Sachin Shah, Ziyuan Qu, Adithya Pediredla, Christopher A. Metzler
arXiv:2610.04606v2 Announce Type: replace
Abstract: We present a 4D Gaussian Splatting framework for the Sparse-View Track of the SIGGRAPH Asia 2026 Volumetric Video Challenge, which requires dynamic...
By Shengqi Wang, Zhengxian Yang, Kaiwen Tian, Yang Liu, Bowen Liu, Hua Du, Taicheng Huang, Jiamin Wu, Tao Yu
arXiv:2611.07665v1 Announce Type: new
Abstract: Capturing scenes with extreme motion and high dynamic range is fundamentally constrained by trade-offs among temporal resolution, spatial detail, signa...
By Yapeng Meng, Yuguo Chen, Xiangru Chen, Shaojun Xu, Xuezhen Wang, Lijian Wang, Zheyu Yang, Yihan Lin, Luping Shi, Rong Zhao
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:2606. 26636v1 Announce Type: cross Abstract: Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at scale because specialized sensors, careful synchronization, and task-specific annotations are required.
By Langyi Chen, Chuanzhi Xu, Haoxian Zhou, Pengfei Ye, Ziyu Luo, Haodong Chen, Qiang Qu, Xiaoming Chen, Weidong Cai
PointEvent introduces serialized motion evidence accumulation for event-based tiny object detection, treating motion continuity as an ordered evidence propagation process. The method organizes event streams into locality‑preserving spatiotemporal paths and chronology‑preserving temporal paths, alternating serialized scans across complementary orders to consolidate fragmented motion evidence. A lightweight event‑wise state‑space framework with a high‑resolution event branch and compact context modulation achieves state‑of‑the‑art performance with the fewest parameters and fastest inference among compared methods.
By Zongze Wu, Baofeng Jia, Weiqi Yan, Jingyuan Zhang, Yu Zang, Xiaoyu Chen, Jing Han
arXiv:2608.27584v1 Announce Type: new
Abstract: Autonomous systems rely on extracting information from light, yet remain brittle in extreme environments, from nighttime navigation to high-speed robot...
By Varun Sundar, Pavan Thodima, Sacha Jungerman, Mohit Gupta
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
HIVE-Diff is an event-conditioned video diffusion framework that reconstructs high‑frame‑rate HDR video from a single low‑dynamic‑range (LDR) reference frame and event data. It separates the task into event‑guided temporal generation and HDR content completion, enabling the creation of multiple HDR frames from one LDR observation. The model is trained on synthetic data and adapted to real events using teacher‑student distillation with LDR‑event sequences as proxy supervision, achieving strong reconstruction quality across synthetic and real datasets.
arXiv:2605. 03337v3 Announce Type: replace-cross Abstract: Recent progress in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction results.
By Lucas Yunkyu Lee, Soonho Kim, Youngwook Kim, Sangmin Kim, Jaesik Park