Michael Steiner

Lukas Radl, Andreas Kurz, Michael Steiner et al.

Modern Neural Radiance Fields (NeRFs) learn a mapping from position to volumetric density leveraging proposal network samplers. In contrast to the coarse-to-fine sampling approach with two NeRFs, this offers significant potential for acceleration using lower network capacity. Given that NeRFs utilize most of their network capacity to estimate radiance, they could store valuable density information in their parameters or their deep features. To investigate this proposition, we take one step back and analyze large, trained ReLU-MLPs used in coarse-to-fine sampling. Building on our novel activation visualization method, we find that trained NeRFs, Mip-NeRFs and proposal network samplers map samples with high density to local minima along a ray in activation feature space. We show how these large MLPs can be accelerated by transforming intermediate activations to a weight estimate, without any modifications to the training protocol or the network architecture. With our approach, we can reduce the computational requirements of trained NeRFs by up to 50% with only a slight hit in rendering quality. Extensive experimental evaluation on a variety of datasets and architectures demonstrates the effectiveness of our approach. Consequently, our methodology provides valuable insight into the inner workings of NeRFs.

Lukas Radl, Felix Windisch, Andreas Kurz et al.

Recently, 3D Gaussian Splatting (3DGS) greatly accelerated mesh extraction from posed images due to its explicit representation and fast software rasterization. While the addition of geometric losses and other priors has improved the accuracy of extracted surfaces, mesh extraction remains difficult in scenes with abundant view-dependent effects. To resolve the resulting ambiguities, prior works rely on multi-view techniques, iterative mesh extraction, or large pre-trained models, sacrificing the inherent efficiency of 3DGS. In this work, we present a simple and efficient alternative by introducing a self-supervised confidence framework to 3DGS: within this framework, learnable confidence values dynamically balance photometric and geometric supervision. Extending our confidence-driven formulation, we introduce losses which penalize per-primitive color and normal variance and demonstrate their benefits to surface extraction. Finally, we complement the above with an improved appearance model, by decoupling the individual terms of the D-SSIM loss. Our final approach delivers state-of-the-art results for unbounded meshes while remaining highly efficient.

Fritz Alder, N. Asokan, Arseny Kurnikov et al.

Function-as-a-Service (FaaS) is a recent and already very popular paradigm in cloud computing. The function provider need only specify the function to be run, usually in a high-level language like JavaScript, and the service provider orchestrates all the necessary infrastructure and software stacks. The function provider is only billed for the actual computational resources used by the function invocation. Compared to previous cloud paradigms, FaaS requires significantly more fine-grained resource measurement mechanisms, e.g. to measure compute time and memory usage of a single function invocation with sub-second accuracy. Thanks to the short duration and stateless nature of functions, and the availability of multiple open-source frameworks, FaaS enables non-traditional service providers e.g. individuals or data centers with spare capacity. However, this exacerbates the challenge of ensuring that resource consumption is measured accurately and reported reliably. It also raises the issues of ensuring computation is done correctly and minimizing the amount of information leaked to service providers. To address these challenges, we introduce S-FaaS, the first architecture and implementation of FaaS to provide strong security and accountability guarantees backed by Intel SGX. To match the dynamic event-driven nature of FaaS, our design introduces a new key distribution enclave and a novel transitive attestation protocol. A core contribution of S-FaaS is our set of resource measurement mechanisms that securely measure compute time inside an enclave, and actual memory allocations. We have integrated S-FaaS into the popular OpenWhisk FaaS framework. We evaluate the security of our architecture, the accuracy of our resource measurement mechanisms, and the performance of our implementation, showing that our resource measurement mechanisms add less than 6.3% latency on standardized benchmarks.

Mic Bowman, Andrea Miele, Michael Steiner et al.

We present Private Data Objects (PDOs), a technology that enables mutually untrusted parties to run smart contracts over private data. PDOs result from the integration of a distributed ledger and Intel Secure Guard Extensions (SGX). In particular, contracts run off-ledger in secure enclaves using Intel SGX, which preserves data confidentiality, execution integrity and enforces data access policies (as opposed to raw data access). A distributed ledger verifies and records transactions produced by PDOs, in order to provide a single authoritative instance of such objects. This allows contracting parties to retrieve and check data related to contract and enclave instances, as well as to serialize and commit contract state updates. The design and the development of PDOs is an ongoing research effort, and open source code is available and hosted by Hyperledger Labs [5, 7].

Thomas Knauth, Michael Steiner, Somnath Chakrabarti et al.

Intel(R) Software Guard Extensions (Intel(R) SGX) is a promising technology to securely process information in otherwise untrusted environments. An important aspect of Intel SGX is the ability to perform remote attestation to assess the endpoint's trustworthiness. Ultimately, remote attestation will result in an attested secure channel to provision secrets to the enclave. We seamlessly combine Intel SGX remote attestation with the establishment of a standard Transport Layer Security (TLS) connection. Remote attestation is performed during the connection setup. To achieve this, we neither change the TLS protocol, nor do we modify existing protocol implementations. We have prototype implementations for three widely used open-source TLS libraries: OpenSSL, wolfSSL and mbedTLS. We describe the requirements, design and implementation details to seamlessly bind attested TLS endpoints to Intel SGX enclaves.

Lukas Radl, Michael Steiner, Mathias Parger et al.

Gaussian Splatting has emerged as a prominent model for constructing 3D representations from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting rendering pipeline relies on several simplifications. Notably, reducing Gaussian to 2D splats with a single view-space depth introduces popping and blending artifacts during view rotation. Addressing this issue requires accurate per-pixel depth computation, yet a full per-pixel sort proves excessively costly compared to a global sort operation. In this paper, we present a novel hierarchical rasterization approach that systematically resorts and culls splats with minimal processing overhead. Our software rasterizer effectively eliminates popping artifacts and view inconsistencies, as demonstrated through both quantitative and qualitative measurements. Simultaneously, our method mitigates the potential for cheating view-dependent effects with popping, ensuring a more authentic representation. Despite the elimination of cheating, our approach achieves comparable quantitative results for test images, while increasing the consistency for novel view synthesis in motion. Due to its design, our hierarchical approach is only 4% slower on average than the original Gaussian Splatting. Notably, enforcing consistency enables a reduction in the number of Gaussians by approximately half with nearly identical quality and view-consistency. Consequently, rendering performance is nearly doubled, making our approach 1.6x faster than the original Gaussian Splatting, with a 50% reduction in memory requirements.

Xuechang Tu, Lukas Radl, Michael Steiner et al.

3D Gaussian Splatting (3DGS) has rapidly become a leading technique for novel-view synthesis, providing exceptional performance through efficient software-based GPU rasterization. Its versatility enables real-time applications, including on mobile and lower-powered devices. However, 3DGS faces key challenges in virtual reality (VR): (1) temporal artifacts, such as popping during head movements, (2) projection-based distortions that result in disturbing and view-inconsistent floaters, and (3) reduced framerates when rendering large numbers of Gaussians, falling below the critical threshold for VR. Compared to desktop environments, these issues are drastically amplified by large field-of-view, constant head movements, and high resolution of head-mounted displays (HMDs). In this work, we introduce VRSplat: we combine and extend several recent advancements in 3DGS to address challenges of VR holistically. We show how the ideas of Mini-Splatting, StopThePop, and Optimal Projection can complement each other, by modifying the individual techniques and core 3DGS rasterizer. Additionally, we propose an efficient foveated rasterizer that handles focus and peripheral areas in a single GPU launch, avoiding redundant computations and improving GPU utilization. Our method also incorporates a fine-tuning step that optimizes Gaussian parameters based on StopThePop depth evaluations and Optimal Projection. We validate our method through a controlled user study with 25 participants, showing a strong preference for VRSplat over other configurations of Mini-Splatting. VRSplat is the first, systematically evaluated 3DGS approach capable of supporting modern VR applications, achieving 72+ FPS while eliminating popping and stereo-disrupting floaters.

Felix Windisch, Thomas Köhler, Lukas Radl et al.

Gaussian Splatting has emerged as a high-performance technique for novel view synthesis, enabling real-time rendering and high-quality reconstruction of small scenes. However, scaling to larger environments has so far relied on partitioning the scene into chunks -- a strategy that introduces artifacts at chunk boundaries, complicates training across varying scales, and is poorly suited to unstructured scenarios such as city-scale flyovers combined with street-level views. Moreover, rendering remains fundamentally limited by GPU memory, as all visible chunks must reside in VRAM simultaneously. We introduce A LoD of Gaussians, a framework for training and rendering ultra-large-scale Gaussian scenes on a single consumer-grade GPU -- without partitioning. Our method stores the full scene out-of-core (e.g., in CPU memory) and trains a Level-of-Detail (LoD) representation directly, dynamically streaming only the relevant Gaussians. A hybrid data structure combining Gaussian hierarchies with Sequential Point Trees enables efficient, view-dependent LoD selection, while a lightweight caching and view scheduling system exploits temporal coherence to support real-time streaming and rendering. Together, these innovations enable seamless multi-scale reconstruction and interactive visualization of complex scenes -- from broad aerial views to fine-grained ground-level details.

Michael Steiner, Thomas Köhler, Lukas Radl et al.

Although 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction, it still faces challenges such as aliasing, projection artifacts, and view inconsistencies, primarily due to the simplification of treating splats as 2D entities. We argue that incorporating full 3D evaluation of Gaussians throughout the 3DGS pipeline can effectively address these issues while preserving rasterization efficiency. Specifically, we introduce an adaptive 3D smoothing filter to mitigate aliasing and present a stable view-space bounding method that eliminates popping artifacts when Gaussians extend beyond the view frustum. Furthermore, we promote tile-based culling to 3D with screen-space planes, accelerating rendering and reducing sorting costs for hierarchical rasterization. Our method achieves state-of-the-art quality on in-distribution evaluation sets and significantly outperforms other approaches for out-of-distribution views. Our qualitative evaluations further demonstrate the effective removal of aliasing, distortions, and popping artifacts, ensuring real-time, artifact-free rendering.

Lukas Radl, Michael Steiner, Andreas Kurz et al.

Due to the omnipresence of Neural Radiance Fields (NeRFs), the interest towards editable implicit 3D representations has surged over the last years. However, editing implicit or hybrid representations as used for NeRFs is difficult due to the entanglement of appearance and geometry encoded in the model parameters. Despite these challenges, recent research has shown first promising steps towards photorealistic and non-photorealistic appearance edits. The main open issues of related work include limited interactivity, a lack of support for local edits and large memory requirements, rendering them less useful in practice. We address these limitations with LAENeRF, a unified framework for photorealistic and non-photorealistic appearance editing of NeRFs. To tackle local editing, we leverage a voxel grid as starting point for region selection. We learn a mapping from expected ray terminations to final output color, which can optionally be supervised by a style loss, resulting in a framework which can perform photorealistic and non-photorealistic appearance editing of selected regions. Relying on a single point per ray for our mapping, we limit memory requirements and enable fast optimization. To guarantee interactivity, we compose the output color using a set of learned, modifiable base colors, composed with additive layer mixing. Compared to concurrent work, LAENeRF enables recoloring and stylization while keeping processing time low. Furthermore, we demonstrate that our approach surpasses baseline methods both quantitatively and qualitatively.

Lukas Radl, Felix Windisch, Thomas Deixelberger et al.

Recent advances in 3D Gaussian representations have significantly improved the quality and efficiency of image-based scene reconstruction. Their explicit nature facilitates real-time rendering and fast optimization, yet extracting accurate surfaces - particularly in large-scale, unbounded environments - remains a difficult task. Many existing methods rely on approximate depth estimates and global sorting heuristics, which can introduce artifacts and limit the fidelity of the reconstructed mesh. In this paper, we present Sorted Opacity Fields (SOF), a method designed to recover detailed surfaces from 3D Gaussians with both speed and precision. Our approach improves upon prior work by introducing hierarchical resorting and a robust formulation of Gaussian depth, which better aligns with the level-set. To enhance mesh quality, we incorporate a level-set regularizer operating on the opacity field and introduce losses that encourage geometrically-consistent primitive shapes. In addition, we develop a parallelized Marching Tetrahedra algorithm tailored to our opacity formulation, reducing meshing time by up to an order of magnitude. As demonstrated by our quantitative evaluation, SOF achieves higher reconstruction accuracy while cutting total processing time by more than a factor of three. These results mark a step forward in turning efficient Gaussian-based rendering into equally efficient geometry extraction.

Michael Steiner, Zhang Chen, Alexander Richard et al.

A photorealistic and immersive human avatar experience demands capturing fine, person-specific details such as cloth and hair dynamics, subtle facial expressions, and characteristic motion patterns. Achieving this requires large, high-quality datasets, which often introduce ambiguities and spurious correlations when very similar poses correspond to different appearances. Models that fit these details during training can overfit and produce unstable, abrupt appearance changes for novel poses. We propose a 3D Gaussian Splatting avatar model with a spatial MLP backbone that is conditioned on both pose and an appearance latent. The latent is learned during training by an encoder, yielding a compact representation that improves reconstruction quality and helps disambiguate pose-driven renderings. At driving time, our predictor autoregressively infers the latent, producing temporally smooth appearance evolution and improved stability. Overall, our method delivers a robust and practical path to high-fidelity, stable avatar driving.

Ivan De Oliveira Nunes, Karim Eldefrawy, Norrathep Rattanavipanon et al.

In this work, we take the first step towards formal verification of Remote Attestation (RA) by designing and verifying an architecture called VRASED: Verifiable Remote Attestation for Simple Embedded Devices. VRASED instantiates a hybrid (HW/SW) RA co-design aimed at low-end embedded systems, e.g., simple IoT devices. VRASED provides a level of security comparable to HW-based approaches, while relying on SW to minimize additional HW costs. Since security properties must be jointly guaranteed by HW and SW, verification is a challenging task, which has never been attempted before in the context of RA. We believe that VRASED is the first formally verified RA scheme. To the best of our knowledge, it is also the first formal verification of a HW/SW implementation of any security service. To demonstrate VRASED's practicality and low overhead, we instantiate and evaluate it on a commodity platform (TI MSP430). VRASED's publicly available implementation was deployed on the Basys3 FPGA.