The simultaneity of complementary conditions:re-integrating and balancing analogue and digital matter(s) in basic architectural education

The actual, globally established, general digital procedures in basic architectural education,producing well-behaved, seemingly attractive up-to-date projects, spaces and first general-researchon all scale levels, apparently present a certain growing amount of deficiencies. These limitations surface only gradually, as the state of things on overall extents is generally deemed satisfactory. Some skills, such as “old-fashioned” analogue drawing are gradually eased-out ofundergraduate curricula and overall modus-operandi, due to their apparent slow inefficiencies in regard to various digital media’s rapid readiness, malleability and unproblematic, quotidian availabilities. While this state of things is understandable, it nevertheless presents a definite challenge. The challenge of questioning how the assessment of conditions and especially their representation,is conducted, prior to contextual architectural action(s) of any kind

Unsupervised Object-Centric Voxelization for Dynamic Scene Understanding

Understanding the compositional dynamics of multiple objects in unsupervised visual environments is challenging, and existing object-centric representation learning methods often ignore 3D consistency in scene decomposition. We propose DynaVol, an inverse graphics approach that learns object-centric volumetric representations in a neural rendering framework. DynaVol maintains time-varying 3D voxel grids that explicitly represent the probability of each spatial location belonging to different objects, and decouple temporal dynamics and spatial information by learning a canonical-space deformation field. To optimize the volumetric features, we embed them into a fully differentiable neural network, binding them to object-centric global features and then driving a compositional NeRF for scene reconstruction. DynaVol outperforms existing methods in novel view synthesis and unsupervised scene decomposition and allows for the editing of dynamic scenes, such as adding, deleting, replacing objects, and modifying their trajectories

Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement

Human perception reliably identifies movable and immovable parts of 3D scenes, and completes the 3D structure of objects and background from incomplete observations. We learn this skill not via labeled examples, but simply by observing objects move. In this work, we propose an approach that observes unlabeled multi-view videos at training time and learns to map a single image observation of a complex scene, such as a street with cars, to a 3D neural scene representation that is disentangled into movable and immovable parts while plausibly completing its 3D structure. We separately parameterize movable and immovable scene parts via 2D neural ground plans. These ground plans are 2D grids of features aligned with the ground plane that can be locally decoded into 3D neural radiance fields. Our model is trained self-supervised via neural rendering. We demonstrate that the structure inherent to our disentangled 3D representation enables a variety of downstream tasks in street-scale 3D scenes using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance segmentation, and 3D bounding box prediction, highlighting its value as a backbone for data-efficient 3D scene understanding models. This disentanglement further enables scene editing via object manipulation such as deletion, insertion, and rigid-body motion.Comment: Project page: https://prafullsharma.net/see3d

Enhancing Interpretable Object Abstraction via Clustering-based Slot Initialization

Object-centric representations using slots have shown the advances towards efficient, flexible and interpretable abstraction from low-level perceptual features in a compositional scene. Current approaches randomize the initial state of slots followed by an iterative refinement. As we show in this paper, the random slot initialization significantly affects the accuracy of the final slot prediction. Moreover, current approaches require a predetermined number of slots from prior knowledge of the data, which limits the applicability in the real world. In our work, we initialize the slot representations with clustering algorithms conditioned on the perceptual input features. This requires an additional layer in the architecture to initialize the slots given the identified clusters. We design permutation invariant and permutation equivariant versions of this layer to enable the exchangeable slot representations after clustering. Additionally, we employ mean-shift clustering to automatically identify the number of slots for a given scene. We evaluate our method on object discovery and novel view synthesis tasks with various datasets. The results show that our method outperforms prior works consistently, especially for complex scenes

Reducing Occlusion in Cinema Databases through Feature-Centric Visualizations

In modern supercomputer architectures, the I/O capabilities do not keep up with the computational speed. Image-based techniques are one very promising approach to a scalable output format for visual analysis, in which a reduced output that corresponds to the visible state of the simulation is rendered in-situ and stored to disk. These techniques can support interactive exploration of the data through image compositing and other methods, but automatic methods of highlighting data and reducing clutter can make these methods more effective. In this paper, we suggest a method of assisted exploration through the combination of feature-centric analysis with image space techniques and show how the reduction of the data to features of interest reduces occlusion in the output for a set of example applications

Towards System Agnostic Calibration of Optical See-Through Head-Mounted Displays for Augmented Reality

This dissertation examines the developments and progress of spatial calibration procedures for Optical See-Through (OST) Head-Mounted Display (HMD) devices for visual Augmented Reality (AR) applications. Rapid developments in commercial AR systems have created an explosion of OST device options for not only research and industrial purposes, but also the consumer market as well. This expansion in hardware availability is equally matched by a need for intuitive standardized calibration procedures that are not only easily completed by novice users, but which are also readily applicable across the largest range of hardware options. This demand for robust uniform calibration schemes is the driving motive behind the original contributions offered within this work. A review of prior surveys and canonical description for AR and OST display developments is provided before narrowing the contextual scope to the research questions evolving within the calibration domain. Both established and state of the art calibration techniques and their general implementations are explored, along with prior user study assessments and the prevailing evaluation metrics and practices employed within. The original contributions begin with a user study evaluation comparing and contrasting the accuracy and precision of an established manual calibration method against a state of the art semi-automatic technique. This is the first formal evaluation of any non-manual approach and provides insight into the current usability limitations of present techniques and the complexities of next generation methods yet to be solved. The second study investigates the viability of a user-centric approach to OST HMD calibration through novel adaptation of manual calibration to consumer level hardware. Additional contributions describe the development of a complete demonstration application incorporating user-centric methods, a novel strategy for visualizing both calibration results and registration error from the user’s perspective, as well as a robust intuitive presentation style for binocular manual calibration. The final study provides further investigation into the accuracy differences observed between user-centric and environment-centric methodologies. The dissertation concludes with a summarization of the contribution outcomes and their impact on existing AR systems and research endeavors, as well as a short look ahead into future extensions and paths that continued calibration research should explore

A view-based deformation tool-kit, Master\u27s Thesis, August 2006

Camera manipulation is a hard problem since a graphics camera is defined by specifying 11 independent parameters. Manipulating such a high-dimensional space to accomplish specific tasks is difficult and requires a certain amount of expertise. We present an intuitive interface that allows novice users to perform camera operations in terms of the change they want see in the image. In addition to developing a natural means for camera interaction, our system also includes a novel interface for viewing and organizing previously saved views. When exploring complex 3D data-sets a single view is not sufficient. Instead, a composite view built from multiple views may be more useful. While changing a single camera is hard enough, manipulating several cameras in a single scene is still harder. In this thesis, we also present a framework for creating composite views and an interface that allows users to manipulate such views in real-time