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researcher

M. Savva

72 papers hereh-index 4729.5k citations118 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author51
  • last author18

Across the 72 of 72 papers where every author was matched, so the position is known.

fields
  • cs.CV49
  • cs.GR11
  • cs.LG4
  • cs.RO4
  • cs.AI3
  • cs.CL1
same name
  • M. Savva — 3 papers, h 4
  • M. Savva — 1 paper, h 4
  • M. Savva — 1 paper, h 15

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152026
most citedShapeNet: An Information-Rich 3D Model Repository

2.4k citations · 5.1k across the 67 of their papers we have counts for

collaborators
Showing 2025 · cs.GRShow all

4 papers · 2 filters

cs.GR2025

iTACO: Interactable Digital Twins of Articulated Objects from Casually Captured RGBD Videos

Weikun Peng, Jun Lv, Cewu Lu +1

Articulated objects are prevalent in daily life. Interactable digital twins of such objects have numerous applications in embodied AI and robotics. Unfortunately, current methods t…

cs.GR2025

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…

cs.GR2025

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…

cs.GR2025

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.