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1. FreeCam3D: Snapshot Structured Light 3D with Freely-Moving Cameras

   https://doi.org/10.1007/978-3-030-58583-9_19  

   Wu, Y. (November 2020, FreeCam3D: Snapshot Structured Light 3D with Freely-Moving Cameras)

   null (Ed.)

   A 3D imaging and mapping system that can handle both multiple-viewers and dynamic-objects is attractive for many applications. We propose a freeform structured light system that does not rigidly constrain camera(s) to the projector. By introducing an optimized phase-coded aperture in the projector, we transform the projector pattern to encode depth in its defocus robustly; this allows a camera to estimate depth, in projector coordinates, using local information. Additionally, we project a Kronecker-multiplexed pattern that provides global context to establish correspondence between camera and projector pixels. Together with aperture coding and projected pattern, the projector offers a unique 3D labeling for every location of the scene. The projected pattern can be observed in part or full by any camera, to reconstruct both the 3D map of the scene and the camera pose in the projector coordinates. This system is optimized using a fully differentiable rendering model and a CNN-based reconstruction. We build a prototype and demonstrate high-quality 3D reconstruction with an unconstrained camera, for both dynamic scenes and multi-camera systems. 

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2. ChromaBlur: Rendering chromatic eye aberration improves accommodation and realism

   https://doi.org/http://dx.doi.org/10.1145/3130800.3130815  

   Cholewiak, S.A.; Love, G.D.; Srinivasan, P.P.; Ng, R.; Banks, M.S. (October 2017, ACM transactions on graphics)

   Computer-graphics engineers and vision scientists want to generate images that reproduce realistic depth-dependent blur. Current rendering algorithms take into account scene geometry, aperture size, and focal distance, and they produce photorealistic imagery as with a high-quality camera. But to create immersive experiences, rendering algorithms should aim instead for perceptual realism. In so doing, they should take into account the significant optical aberrations of the human eye. We developed a method that, by incorporating some of those aberrations, yields displayed images that produce retinal images much closer to the ones that occur in natural viewing. In particular, we create displayed images taking the eye’s chromatic aberration into account. This produces different chromatic effects in the retinal image for objects farther or nearer than current focus. We call the method ChromaBlur. We conducted two experiments that illustrate the benefits of ChromaBlur. One showed that accommodation (eye focusing) is driven quite effectively when ChromaBlur is used and that accommodation is not driven at all when conventional methods are used. The second showed that perceived depth and realism are greater with imagery created by ChromaBlur than in imagery created conventionally. ChromaBlur can be coupled with focus-adjustable lenses and gaze tracking to reproduce the natural relationship between accommodation and blur in HMDs and other immersive devices. It may thereby minimize the adverse effects of vergence-accommodation conflicts. 

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3. Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging

   Ghanekar, Bhargav; Khan, Salman S; Sharma, Pranav; Singh, Shreyas; Boominathan, Vivek; Mitra, Kaushik; Veeraraghavan, Ashok (March 2024, ArXiv)

   Passive, compact, single-shot 3D sensing is useful in many application areas such as microscopy, medical imaging, surgical navigation, and autonomous driving where form factor, time, and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance, in an ultra-compact form factor, and in a passive, snapshot manner is challenging. Dual-pixel (DP) sensors are a potential solution to achieve the same. DP sensors collect light rays from two different halves of the lens in two interleaved pixel arrays, thus capturing two slightly different views of the scene, like a stereo camera system. However, imaging with a DP sensor implies that the defocus blur size is directly proportional to the disparity seen between the views. This creates a trade-off between disparity estimation vs. deblurring accuracy. To improve this trade-off effect, we propose CADS (Coded Aperture Dual-Pixel Sensing), in which we use a coded aperture in the imaging lens along with a DP sensor. In our approach, we jointly learn an optimal coded pattern and the reconstruction algorithm in an end-to-end optimization setting. Our resulting CADS imaging system demonstrates improvement of >1.5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings. Furthermore, we build the proposed CADS prototypes for DSLR photography settings and in an endoscope and a dermoscope form factor. Our novel coded dual-pixel sensing approach demonstrates accurate RGB-D reconstruction results in simulations and real-world experiments in a passive, snapshot, and compact manner. 

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4. Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting

   https://doi.org/10.1109/IROS58592.2024.10802412  

   Swann, Aiden; Strong, Matthew; Do, Won Kyung; Camps, Gadiel Sznaier; Schwager, Mac; Kennedy, Monroe (October 2024, IEEE)

   In this work, we propose a novel method to supervise 3D Gaussian Splatting (3DGS) scenes using optical tactile sensors. Optical tactile sensors have become widespread in their use in robotics for manipulation and object representation; however, raw optical tactile sensor data is unsuitable to directly supervise a 3DGS scene. Our representation leverages a Gaussian Process Implicit Surface to implicitly represent the object, combining many touches into a unified representation with uncertainty. We merge this model with a monocular depth estimation network, which is aligned in a two stage process, coarsely aligning with a depth camera and then finely adjusting to match our touch data. For every training image, our method produces a corresponding fused depth and uncertainty map. Utilizing this additional information, we propose a new loss function, variance weighted depth supervised loss, for training the 3DGS scene model. We leverage the DenseTact optical tactile sensor and RealSense RGB-D camera to show that combining touch and vision in this manner leads to quantitatively and qualitatively better results than vision or touch alone in a few-view scene syntheses on opaque as well as on reflective and transparent objects. Please see our project page at armlabstanford.github.io/touch-gs 

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5. Defocus Map Estimation and Deblurring From a Single Dual-Pixel Image

   https://doi.org/10.1109/ICCV48922.2021.00223  

   Xin, Shumian; Wadhwa, Neal; Xue, Tianfan; Barron, Jonathan T.; Srinivasan, Pratul P.; Chen, Jiawen; Gkioulekas, Ioannis; Garg, Rahul (January 2021, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021)

   We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map---the amount of defocus blur at each pixel---and recovers an all-in-focus image. Our method is inspired from recent works that leverage the dual-pixel sensors available in many consumer cameras to assist with autofocus, and use them for recovery of defocus maps or all-in-focus images. These prior works have solved the two recovery problems independently of each other, and often require large labeled datasets for supervised training. By contrast, we show that it is beneficial to treat these two closely-connected problems simultaneously. To this end, we set up an optimization problem that, by carefully modeling the optics of dual-pixel images, jointly solves both problems. We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised. 

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