19 papers
Learning to Place Objects with Programs and Iterative Self Training
Adrian Chang, Kai Wang, Yuanbo Li +3
In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within the scene. Empirical observations of d…
SceneEval: Evaluating Semantic Coherence in Text-Conditioned 3D Indoor Scene Synthesis
Hou In Ivan Tam, Hou In Derek Pun, Austin T. Wang +2
Despite recent advances in text-conditioned 3D indoor scene generation, there remain gaps in the evaluation of these methods. Existing metrics often measure realism by comparing ge…
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
ZeMing Gong, Austin T. Wang, Xiaoliang Huo +4
Measuring biodiversity is crucial for understanding ecosystem health. While prior works have developed machine learning models for taxonomic classification of photographic images a…
HSM: Hierarchical Scene Motifs for Multi-Scale Indoor Scene Generation
Hou In Derek Pun, Hou In Ivan Tam, Austin T. Wang +3
Despite advances in indoor 3D scene layout generation, synthesizing scenes with dense object arrangements remains challenging. Existing methods focus on large furniture while negle…
MLFM: Multi-Layered Feature Maps for Richer Language Understanding in Zero-Shot Semantic Navigation
Sonia Raychaudhuri, Enrico Cancelli, Tommaso Campari +3
Recent progress in large vision-language models has driven improvements in language-based semantic navigation, where an embodied agent must reach a target object described in natur…
SemLayoutDiff: Semantic Layout Generation with Diffusion Model for Indoor Scene Synthesis
Xiaohao Sun, Divyam Goel, Angel X. Chang
We present SemLayoutDiff, a unified model for synthesizing diverse 3D indoor scenes across multiple room types. The model introduces a scene layout representation combining a top-d…