Inverse rendering seeks to estimate scene characteristics from a set of data images. The dominant approach is based on differential rendering using Monte-Carlo. Algorithms as such usually rely on a forward model and use an iterative gradient method that requires sampling millions of light paths per iteration. This paper presents an efficient framework that speeds up existing inverse rendering algorithms. This is achieved by tailoring the iterative process of inverse rendering specifically to a GPU architecture. For this cause, we introduce two interleaved steps - Path Sorting and Path Recycling. Path Sorting allows the GPU to deal with light paths of the same size. Path Recycling allows the algorithm to use light paths from previous iterations to better utilize the information they encode. Together, these steps significantly speed up gradient optimization. In this paper, we give the theoretical background for Path Recycling. We demonstrate its efficiency for volumetric scattering tomography and reflectometry (surface reflections).
Neural Radiance Fields (NeRF) have demonstrated superior novel view synthesis performance but are slow at rendering. To speed up the volume rendering process, many acceleration methods have been proposed at the cost of large memory consumption. To push the frontier of the efficiency-memory trade-off, we explore a new perspective to accelerate NeRF rendering, leveraging a key fact that the viewpoint change is usually smooth and continuous in interactive viewpoint control. This allows us to leverage the information of preceding viewpoints to reduce the number of rendered pixels as well as the nu…
Swiss Federal Institute of Technology in Zürich（苏黎世联邦理工学院）；The University of Queensland（昆士兰大学）
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels. While impressive, these methods still require a relatively large amount of supervision and segmenting an object can take several minutes in practice. Such systems typically only optimize their representation on the particular scene they are fitting, without leveraging any prior information from previously seen images. In this paper, we propose to use features extracted with models trained on large existing datasets …
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Yandex（扬捷克斯）；Artificial Intelligence Research Institute（人工智能研究院）
We present a new system (NPBG++) for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently leverages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graphics in several important ways. By predicting the descriptors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suitable for scenes with strong non-Lam…
BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China（人工智能学院，北京师范大学，北京）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China（人工智能与未来网络研究院，北京师范大学，珠海）
Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Computer Science（计算机科学系）
Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院智能绿色车辆与交通国家重点实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
