4,098 research outputs found
Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases
The proliferation of technologies, such as extended reality (XR), has
increased the demand for high-quality three-dimensional (3D) graphical
representations. Industrial 3D applications encompass computer-aided design
(CAD), finite element analysis (FEA), scanning, and robotics. However, current
methods employed for industrial 3D representations suffer from high
implementation costs and reliance on manual human input for accurate 3D
modeling. To address these challenges, neural radiance fields (NeRFs) have
emerged as a promising approach for learning 3D scene representations based on
provided training 2D images. Despite a growing interest in NeRFs, their
potential applications in various industrial subdomains are still unexplored.
In this paper, we deliver a comprehensive examination of NeRF industrial
applications while also providing direction for future research endeavors. We
also present a series of proof-of-concept experiments that demonstrate the
potential of NeRFs in the industrial domain. These experiments include
NeRF-based video compression techniques and using NeRFs for 3D motion
estimation in the context of collision avoidance. In the video compression
experiment, our results show compression savings up to 48\% and 74\% for
resolutions of 1920x1080 and 300x168, respectively. The motion estimation
experiment used a 3D animation of a robotic arm to train Dynamic-NeRF (D-NeRF)
and achieved an average peak signal-to-noise ratio (PSNR) of disparity map with
the value of 23 dB and an structural similarity index measure (SSIM) 0.97
The Iray Light Transport Simulation and Rendering System
While ray tracing has become increasingly common and path tracing is well
understood by now, a major challenge lies in crafting an easy-to-use and
efficient system implementing these technologies. Following a purely
physically-based paradigm while still allowing for artistic workflows, the Iray
light transport simulation and rendering system allows for rendering complex
scenes by the push of a button and thus makes accurate light transport
simulation widely available. In this document we discuss the challenges and
implementation choices that follow from our primary design decisions,
demonstrating that such a rendering system can be made a practical, scalable,
and efficient real-world application that has been adopted by various companies
across many fields and is in use by many industry professionals today
Path Guiding with Vertex Triplet Distributions
Good importance sampling strategies are decisive for the quality and robustness of photorealistic image synthesis with Monte Carlo integration. Path guiding approaches use transport paths sampled by an existing base sampler to build and refine a guiding distribution. This distribution then guides subsequent paths in regions that are otherwise hard to sample. We observe that all terms in the measurement contribution function sampled during path construction depend on at most three consecutive path vertices. We thus propose to build a 9D guiding distribution over vertex triplets that adapts to the full measurement contribution with a 9D Gaussian mixture model (GMM). For incremental path sampling, we query the model for the last two vertices of a path prefix, resulting in a 3D conditional distribution with which we sample the next vertex along the path. To make this approach scalable, we partition the scene with an octree and learn a local GMM for each leaf separately. In a learning phase, we sample paths using the current guiding distribution and collect triplets of path vertices. We resample these triplets online and keep only a fixed-size subset in reservoirs. After each progression, we obtain new GMMs from triplet samples by an initial hard clustering followed by expectation maximization. Since we model 3D vertex positions, our guiding distribution naturally extends to participating media. In addition, the symmetry in the GMM allows us to query it for paths constructed by a light tracer. Therefore our method can guide both a path tracer and light tracer from a jointly learned guiding distribution
- …