Cross-spectral Gated-RGB Stereo Depth Estimation
원문 초록
Gated cameras flood-illuminate a scene and capture the time-gated impulse response of a scene. By employing nanosecond-scale gates, existing sensors are capable of capturing mega-pixel gated images, delivering dense depth improving on today's LiDAR sensors in spatial resolution and depth precision. Although gated depth estimation methods deliver a million of depth estimates per frame, their resolution is still an order below existing RGB imaging methods. In this work, we combine high-resolution stereo HDR RCCB cameras with gated imaging, allowing us to exploit depth cues from active gating, multi-view RGB and multi-view NIR sensing -- multi-view and gated cues across the entire spectrum. The resulting capture system consists only of low-cost CMOS sensors and flood-illumination. We propose a novel stereo-depth estimation method that is capable of exploiting these multi-modal multi-view depth cues, including the active illumination that is measured by the RCCB camera when removing the IR-cut filter. The proposed method achieves accurate depth at long ranges, outperforming the next best existing method by 39% for ranges of 100 to 220m in MAE on accumulated LiDAR ground-truth. Our code, models and datasets are available at https://light.princeton.edu/gatedrccbstereo/ .
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges
FoundationStereo: Zero-Shot Stereo Matching
Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
Cameras as Relative Positional Encoding
SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams
토론
이 논문과 연결된 포럼 글이 아직 없습니다.