Remote direct memory access (RDMA) is widely deployed in production data centers to enable low-latency transmission. The current multipath RDMA transmission protocols effectively improve link utilization by allocating traffic to equal-cost parallel paths. To address packet reordering, they struggle to control the level of out-of-order packets by using bitmaps. However, under asymmetric path status and highly dynamic traffic scenarios, a large number of out-of-order packets easily cause bitmap overflow and frequent unnecessary retransmission, resulting in goodput far below throughput. Motivated by this, we present MPTR, an efficient multipath transport with robust reordering for RDMA networks. At its core, MPTR continuously monitors the multipath congestion status at the receiver and distributes the traffic in a congestion-aware manner to proactively reduce the degree of out-of-order and avoid triggering retransmission due to bitmap cache overflow. The NS-3 simulation results show that MPTR effectively reduces unnecessary retransmission and improves goodput under realistic workloads by up to 34%, 49%, and 51% compared to multi-path remote direct memory access (MP-RDMA), ConWeave, and data center quantized congestion notification (DCQCN), respectively.

High quality and high speed videography using Non-Line-of-Sight (NLOS) imaging benefit autonomous navigation, collision prevention, and post-disaster search and rescue tasks. Current solutions have to balance between the frame rate and image quality. High frame rates, for example, can be achieved by reducing either per-point scanning time or scanning density, but at the cost of lowering the information density at individual frames. Fast scanning process further reduces the signal-to-noise ratio and different scanning systems exhibit different distortion characteristics. In this work, we design and employ a new Transient Transformer architecture called TransiT to achieve real-time NLOS recovery under fast scans. TransiT directly compresses the temporal dimension of input transients to extract features, reducing computation costs and meeting high frame rate requirements. It further adopts a feature fusion mechanism as well as employs a spatial-temporal Transformer to help capture features of NLOS transient videos. Moreover, TransiT applies transfer learning to bridge the gap between synthetic and real-measured data. In real experiments, TransiT manages to reconstruct from sparse transients of $16 \times 16$ measured at an exposure time of 0.4 ms per point to NLOS videos at a $64 \times 64$ resolution at 10 frames per second. We will make our code and dataset available to the community.

Remote Direct Memory Access (RDMA) is widely deployed in datacenter networks (DCNs) due to its ultra-low latency, high throughput, and low CPU overhead. Since RDMA is sensitive to out-of-order packets, the previous load balancing schemes designed based on TCP do not work well in RDMA networks. Recently proposed load balancing schemes focus on solving packet reordering within the network. However, the existing solutions cannot be extended in practice because the required queues far exceed common switch capabilities. In this paper, we propose a scalable and efficient load balancing called SELB to improve availability and scalability. SELB employs a clustering algorithm to categorize equal-cost paths and then reroutes traffic to the same cluster parallel paths to reduce the degree of out-of-order and improve queue utilization. The NS-3 simulation results demonstrate that SELB reduces the average flow completion time (FCT) and the 99th percentile FCT by up to 33% and 21%, respectively, compared to the state-of-the-art load balancing schemes.

Reconstructing an object behind a scattering medium needs to tackle different light scatterings inside the medium and in the free space. Major approaches, e.g., diffuse optical tomography and non-line-of-sight imaging, address either of the light scatterings. Confocal diffuse tomography (CDT) considers the media acting as a diffuse kernel on the free-space scattering and recovers the object by deconvolving the inside-medium scattering. Inspired by CDT, we present a Neural De-Scatterer to solve this challenging problem. We exploit neural implicit fields to represent the the free-space scattering and use a multilayer perceptron (MLP) to learn the density and albedo of the hidden object. Furthermore, we tailor a bi-directional training strategy to optimize the MLP with forward and backward losses and employ hash encoding for memory and computation efficiency. The Neural De-Scatterer enables us to reconstruct objects at arbitrary resolution. Comprehensive experiments with synthetic and real measurements demonstrate that our Neural De-Scatterer outperforms state-of-the-art methods. Our data and code are publicly available.

Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem. Most physics-based NLOS imaging methods address the complexity of this problem by assuming three-bounce reflections and no self-occlusion. However, these assumptions may break down for objects with large depth variations, preventing physics-based algorithms from accurately reconstructing the details and high-frequency information. On the other hand, while learning-based methods can avoid these assumptions, they may struggle to reconstruct details without specific designs due to the spectral bias of neural networks. To overcome these issues, we propose a novel approach that enhances physics-based NLOS imaging methods by introducing a learnable inverse kernel in the Fourier domain and using an attention mechanism to improve the neural network to learn high-frequency information. Our method is evaluated on publicly available and new synthetic datasets, demonstrating its commendable performance compared to prior physics-based and learning-based methods, especially for objects with large depth variations. Moreover, our approach generalizes well to real data and can be applied to tasks such as classification and depth reconstruction. We will make our code and dataset publicly available: https://sci2020.github.io.

Nearly all existing human pose estimation techniques address the problem under the line-of-sight (LOS) setting. Many real-life applications such as rescue missions and autonomous driving, in contrast, require estimating the pose of hidden subjects. In this paper, we present a non-line-of-sight (NLOS) pose estimator, which produces a skeletal representation of hidden human poses. A brute-force approach would first conduct albedo reconstruction of a hidden subject and then apply LOS pose estimation. We show that such an implementation does not effectively exploit features unique to NLOS and subsequently yields artifacts such as missing joints. We instead first generate a comprehensive NLOS human pose dataset of 19 subjects under 9 motions. We then present a spatially aware deep learning technique based on convolutional neural networks that explicitly employ NLOS features. Comprehensive experiments on both synthetic and real data show that our new estimator is both effective and robust and can be seamlessly integrated into learning-based NLOS scene reconstruction. Our HiddenPose transient dataset contains synthetic transients with ground-truths of the volumes and the joints and real-world transients captured from our NLOS imaging system. Extensive assessments demonstrate that the HiddenPose transient dataset is valuable for effective NLOS research. We will make our data and code publicly available.

We present a neural modeling framework for non-line-of-sight (NLOS) imaging. Previous solutions have sought to explicitly recover the 3D geometry (e.g., as point clouds) or voxel density (e.g., within a pre-defined volume) of the hidden scene. In contrast, inspired by the recent Neural Radiance Field (NeRF) approach, we use a multi-layer perceptron (MLP) to represent the neural transient field or NeTF. However, NeTF measures the transient over spherical wavefronts rather than the radiance along lines. We therefore formulate a spherical volume NeTF reconstruction pipeline, applicable to both confocal and non-confocal setups. Compared with NeRF, NeTF samples a much sparser set of viewpoints (scanning spots) and the sampling is highly uneven. We thus introduce a Monte Carlo technique to improve the robustness in the reconstruction. Experiments on synthetic and real datasets demonstrate NeTF achieves state-of-the-art performance and can provide reliable reconstructions even under semi-occlusions and on non-Lambertian materials.