# Generating Context-Aware Natural Answers for Questions in 3D Scenes

Source: [Generating Context-Aware Natural Answers for Questions in 3D Scenes](<https://www.sota2.com/research/paper/68fa71061e209131cea4ea6e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:55:42.510Z

Authors: Mohammed Munzer Dwedari, Matthias Niessner, Dave Zhenyu Chen

Published: 2023-10-30T13:18:31.000Z

## Abstract

3D question answering is a young field in 3D vision-language that is yet to be explored. Previous methods are limited to a pre-defined answer space and cannot generate answers naturally. In this work, we pivot the question answering task to a sequence generation task to generate free-form natural answers for questions in 3D scenes (Gen3DQA). To this end, we optimize our model directly on the language rewards to secure the global sentence semantics. Here, we also adapt a pragmatic language understanding reward to further improve the sentence quality. Our method sets a new SOTA on the ScanQA benchmark (CIDEr score 72.22/66.57 on the test sets).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.19516>)

[PDF](<https://arxiv.org/pdf/2310.19516.pdf>)