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Exploring 2D backbone effects for indoor semantic occupancy prediction

Paper recorded by Signals 4 on 2026-09-15 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.CV · 计算机视觉 · first seen 2026-09-16

Abstract

Semantic occupancy prediction gives an embodied agent a voxel-level account of where space is free, occupied, and semantically meaningful. In RGB-D pipelines such as EmbodiedScan, the image encoder is often left as a default module, even though its features are the visual evidence later sampled into the 3D grid. We study this design choice directly. A central finding is that changing the 2D backbo

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#37 most recent of 237 cs.CV papers we have recorded · ↑ newer: Semantic-Spatial Agreement Verification for Mitigating Object Hallucin · ↓ older: Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evi
Cite this page: Exploring 2D backbone effects for indoor semantic occupancy prediction: the #37 most recent of 237 cs.CV papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/exploring-2d-backbone-effects-for-indoor-semantic-occupancy-prediction.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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