https://github.com/LZ-CH/DSPNet
공식 코드DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering
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A 3D-QA task requires models to answer a question when given all the information of a 3D scene. Here, models use the 3D spatial information, such as RGB-D scans or point cloud data. We also require models to specify the 3D-bounding boxes of objects that are related to this question answering. This prevents models from answering questions by relying on the textual priors of the trained questions without examining the scene. However, unlike 3D dense captioning, we do not require models to target one described object for each question. This is because multiple objects can be used to answer certain questions. For example, the question “What color is the chairs around the table?” is related to multiple objects. This question is also answerable as long as the chairs around the unique table in the scene have the same color. In such scenarios, we require models to answer the question addressing multiple 3D-bounding boxes.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering
Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding
ScanQA: 3D Question Answering for Spatial Scene Understanding
Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
PointLLM: Empowering Large Language Models to Understand Point Clouds
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.