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Markus Gross

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4 papers

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NeurIPS Conference 2025 Conference Paper

IPFormer: Visual 3D Panoptic Scene Completion with Context-Adaptive Instance Proposals

Markus Gross
Aya Fahmy
Danit Niwattananan
Dominik Muhle
Rui Song
Daniel Cremers
Henri Meeß

Semantic Scene Completion (SSC) has emerged as a pivotal approach for jointly learning scene geometry and semantics, enabling downstream applications such as navigation in mobile robotics. The recent generalization to Panoptic Scene Completion (PSC) advances the SSC domain by integrating instance-level information, thereby enhancing object-level sensitivity in scene understanding. While PSC was introduced using LiDAR modality, methods based on camera images remain largely unexplored. Moreover, recent Transformer-based approaches utilize a fixed set of learned queries to reconstruct objects within the scene volume. Although these queries are typically updated with image context during training, they remain static at test time, limiting their ability to dynamically adapt specifically to the observed scene. To overcome these limitations, we propose IPFormer, the first method that leverages context-adaptive instance proposals at train and test time to address vision-based 3D Panoptic Scene Completion. Specifically, IPFormer adaptively initializes these queries as panoptic instance proposals derived from image context and further refines them through attention-based encoding and decoding to reason about semantic instance-voxel relationships. Extensive experimental results show that our approach achieves state-of-the-art in-domain performance, exhibits superior zero-shot generalization on out-of-domain data, and achieves a runtime reduction exceeding 14$\times$. These results highlight our introduction of context-adaptive instance proposals as a pioneering effort in addressing vision-based 3D Panoptic Scene Completion. Code available at https: //github. com/markus-42/ipformer.

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NeurIPS Conference 2024 Conference Paper

BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation

Xiang Zhang
Bingxin Ke
Hayko Riemenschneider
Nando Metzger
Anton Obukhov
Markus Gross
Konrad Schindler
Christopher Schroers

By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficient detail. Although recent diffusion-based MDE approaches exhibit a superior ability to extract details, they struggle in geometrically complex scenes that challenge their geometry prior, trained on less diverse 3D data. To leverage the complementary merits of both worlds, we propose BetterDepth to achieve geometrically correct affine-invariant MDE while capturing fine details. Specifically, BetterDepth is a conditional diffusion-based refiner that takes the prediction from pre-trained MDE models as depth conditioning, in which the global depth layout is well-captured, and iteratively refines details based on the input image. For the training of such a refiner, we propose global pre-alignment and local patch masking methods to ensure BetterDepth remains faithful to the depth conditioning while learning to add fine-grained scene details. With efficient training on small-scale synthetic datasets, BetterDepth achieves state-of-the-art zero-shot MDE performance on diverse public datasets and on in-the-wild scenes. Moreover, BetterDepth can improve the performance of other MDE models in a plug-and-play manner without further re-training.

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AAAI Conference 2018 Conference Paper

Computer-Assisted Authoring for Natural Language Story Scripts

Rushit Sanghrajka
Wojciech Witoń
Sasha Schriber
Markus Gross
Mubbasir Kapadia

In order to assist scriptwriters during the process of storywriting, we have developed a system that can extract information from natural language stories, and allow for story-centric as well as character-centric reasoning. These inferencing capabilities are exposed to the user through intuitive querying systems, allowing the scriptwriter to ask the system questions about story and character information. We introduce knowledge bytes as atoms of information and demonstrate that the system can parse text into a stream of knowledge bytes and use these mentioned reasoning capabilities through logical reasoning.

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AAAI Conference 2018 Conference Paper

InspireMe: Learning Sequence Models for Stories

Vincent Fortuin
Romann Weber
Sasha Schriber
Diana Wotruba
Markus Gross

We present a novel approach to modeling stories using recurrent neural networks. Different story features are extracted using natural language processing techniques and used to encode the stories as sequences. These sequences can be learned by deep neural networks, in order to predict the next story events. The predictions can be used as an inspiration for writers who experience a writer’s block. We further assist writers in their creative process by generating visualizations of the character interactions in the story. We show that suggestions from our model are rated as highly as the real scenes from a set of ﬁlms and that our visualizations can help people in gaining deeper story understanding.

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