The rapid adoption of volumetric capture technologies has created a pressing need for efficient storage and streaming of dynamic 3D content. Unfortunately, current compression standards often treat dynamic sequences as independent frames or rely on computationally expensive non-rigid registration, making them unsuitable for real-time applications or large-scale environments. In this paper, we present a novel end-to-end compression framework for dynamic Truncated Signed Distance Field volumes derived from captured 3D content, leveraging a representation that is temporally stable, easily parallelizable, and already widely used in scene reconstruction and volumetric fusion pipelines. We then adapt classic 2D video coding paradigms such as spatial coding via Discrete Cosine Transform and temporal coding using a real-time motion compensation pipeline to provide robust, real-time, and training-free encoding and decoding for 3D content. Extensive evaluations on human performance captures demonstrate that our codec achieves ~35% bitrate savings at equal distortion while operating in real time at 30 FPS, while stronger temporal coherence in large-scale synthetic environments yields up to 12× bitrate reduction at equal distortion.

We propose a novel multi-stage reversible network for human pose estimation, which is called RevPose. The proposed RevPose consists of network modules with a variable number of columns that can be continuously increased. Multi-column reversible connection approach is employed in each sub-network. This approach is distinct from existing traditional networks, as it gradually unfolds the learned information during the forward propagation process across multiple stages while avoiding operations that can lead to loss of feature, such as fusion and downsampling. Our experiments show that the RevPose achieves fascinating performance. For example, with no pre-train, RevPose get 77.3% AP on the human pose estimation on the COCO dataset. It prove the RevPose, as a new try of reversible network, exhibits superior replaceable properties and can be used as a new direction for future research.

Localizing mobile phone users precisely enough to provide AR content in theaters and concert venues is extremely challenging due to dynamic staging and variable lighting. Visual markers are often disruptive in terms of aesthetics, and static pre-defined feature maps are not robust to visual changes. In this paper, we study several techniques that leverage sparse fixed infrastructure to monitor and adapt to changes in the environment at runtime to enable robust AR quality pose tracking for large audiences. Our most basic technique uses one or more fixed cameras in the environment to prune away poor feature points due to motion and lighting from a static model. For more challenging environments, we propose transmitting dynamic 3D feature maps that adapt to changes in the scene in real-time. Users with a mobile phone camera can use these maps to accurately localize across highly dynamic environments without explicit markers. We show the performance trade-offs resulting from StageAR’s different reconstruction techniques, ranging from multiple stereo cameras to cameras paired with LiDAR. We evaluate each approach in our system across a wide variety of simulated and real environments at auditorium/theater scale and find that our most accurate technique can match the performance of large ($1.5 \times 1.5{\mathrm {m}}$) back-lit static markers without being visible to users.

X-ray inspection of pipeline welds brings benefits for the industrial quality and safety of production. However, noise and complex texture structure in X-ray images caused by issues such as poor contrast and blurry defects lead to extraordinary obstacles for lightweight and high-precision identification to satisfy almost 100% accuracy requirements, especially in constrained environments. This paper proposes a novel method, named YOLO-Xweld, to detect pipeline welding defects for constrained application scenarios. YOLO-Xweld is derived from YOLO v3-tiny by adding an SPP module to the backbone network to enhance multi-scale feature extraction, and weakening large-scale network detection branch based on characteristics of pipeline weld X-ray images. Depthwise separable convolution is introduced to achieve a lightweight model and reduce the dependence on the high-performance hardware. Experiments show that the number of the model's parameters and floating-point operations have dropped by over 70%, and the model size has been reduced to 1/15 of the original size, while the test accuracy is close to 100%. This is of great significance in reducing the dependence on high-performance hardware and enhancing the convenience and operability during inspections in the constrained pipeline weld detection environment.