11,973 research outputs found
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
We introduce ScanComplete, a novel data-driven approach for taking an
incomplete 3D scan of a scene as input and predicting a complete 3D model along
with per-voxel semantic labels. The key contribution of our method is its
ability to handle large scenes with varying spatial extent, managing the cubic
growth in data size as scene size increases. To this end, we devise a
fully-convolutional generative 3D CNN model whose filter kernels are invariant
to the overall scene size. The model can be trained on scene subvolumes but
deployed on arbitrarily large scenes at test time. In addition, we propose a
coarse-to-fine inference strategy in order to produce high-resolution output
while also leveraging large input context sizes. In an extensive series of
experiments, we carefully evaluate different model design choices, considering
both deterministic and probabilistic models for completion and semantic
inference. Our results show that we outperform other methods not only in the
size of the environments handled and processing efficiency, but also with
regard to completion quality and semantic segmentation performance by a
significant margin.Comment: Video: https://youtu.be/5s5s8iH0NF
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Knowledge-based vision and simple visual machines
The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
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