Current state-of-the-art novel view synthesis methods generate natural scene images indistinguishable from real images. However, methods developed for ultrasound imaging often struggle with semantic accuracy, physical plausibility, or large domain gaps from real ultrasound images. In Ultra-NeRF, we address these limitations by reconstructing a neural field of acoustic properties and enabling novel view synthesis of ultrasound images through an ultrasound-specific forward synthesis model. While Ultra-NeRF successfully captures key ultrasound characteristics resulting from sound-wave-based imaging, it lacks interpretability in the acoustic parameter space, limiting practical utility and in-depth analysis of the acoustic properties. In this work, we build upon our previous conference paper by shifting the emphasis from generating visually plausible images with Ultra-NeRF to ensuring the physical accuracy of the underlying neural field. To this end, we revisit neural fields for ultrasound and introduce Sonographic Neural Reflection Field which we call UltraSoundNeRF (or USNeRF in short form) by redesigning Ultra-NeRF's differentiable forward synthesis model and incorporating physics-inspired regularization that results from properties of ultrasound imaging. We extend the Ultra-NeRF dataset by introducing experiments on patient lower leg data and two new scenarios: an ex-vivo phantom and a calibration phantom. The ex-vivo phantom demonstrates that the proposed method can reconstruct real biological tissue, while the calibration phantom shows that incorporating regularization yields attenuation estimates that more closely reflect the expected physical values, and experiments on patient lower leg data. While reconstruction accuracy remains comparable to the original method, our approach significantly enhances the interpretability of acoustic properties across materials with diverse characteristics.

Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to enhance safety and efficiency. Existing datasets either provide partial egocentric views or sparse exocentric multi-view context, but do not explore the comprehensive combination of both. We introduce EgoExOR, the first OR dataset and accompanying benchmark to fuse first-person and third-person perspectives. Spanning 94 minutes (84,553 frames at 15 FPS) of two emulated spine procedures, Ultrasound-Guided Needle Insertion and Minimally Invasive Spine Surgery, EgoExOR integrates egocentric data (RGB, gaze, hand tracking, audio) from wearable glasses, exocentric RGB and depth from RGB-D cameras, and ultrasound imagery. Its detailed scene graph annotations, covering 36 entities and 22 relations (568,235 triplets), enable robust modeling of clinical interactions, supporting tasks like action recognition and human-centric perception. We evaluate the surgical scene graph generation performance of two adapted state-of-the-art models and offer a new baseline that explicitly leverages EgoExOR’s multimodal and multi-perspective signals. This new dataset and benchmark set a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception. Our code and data are available at https://github.com/ardamamur/EgoExOR.

Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods, there is a lack of comprehensive facade classes and data covering the architectural variability. In ZAHA11Project page: https://github.com/OloOcki/zaha, we introduce Level of Facade Generalization (LoFG), novel hierarchical facade classes designed based on international urban modeling standards, ensuring compatibility with real-world challenging classes and uniform methods' comparison. Realizing the LoFG, we present to date the largest semantic 3D facade segmentation dataset, providing 601 million annotated points at five and 15 classes of LoFG2 and LoFG3, respectively. More-over, we analyze the performance of baseline semantic segmentation methods on our introduced LoFG classes and data, complementing it with a discussion on the unresolved challenges for facade segmentation. We firmly believe that ZAHA shall facilitate further development of 3D facade semantic segmentation methods, enabling robust segmentation indispensable in creating urban digital twins.