State-of-the-art models on contemporary 3D segmentation benchmarks like ScanNet consume and label dataset- provided 3D point clouds, obtained through post processing of sensed multiview RGB-D images. They are typically trained in-domain, forego large-scale 2D pre-training and outperform alternatives that featurize the posed RGB- D multiview images instead. The gap in performance between methods that consume posed images versus post- processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures. In this paper, we challenge this view and propose ODIN (Omni-Dimensional INstance segmentation), a model that can segment and label both 2D RGB images and 3D point clouds, using a transformer architecture that alternates between 2D within-view and 3D cross-view information fusion. Our model differentiates 2D and 3D feature oper-ations through the positional encodings of the tokens in-volved, which capture pixel coordinates for 2D patch tokens and 3D coordinates for 3D feature tokens. ODIN achieves state-of-the-art performance on ScanNet200, Matterport3D and AI2THOR 3D instance segmentation benchmarks, and competitive performance on ScanNet, S3DIS and COCO. It outperforms all previous works by a wide margin when the sensed 3D point cloud is used in place of the point cloud sampled from 3D mesh. When used as the 3D perception engine in an instructable embodied agent architecture, it sets a new state-of-the-art on the TEACh action-from-dialogue benchmark. Our code and checkpoints can be found at the project website

Diffusion models excel at modeling complex and multi-modal trajectory distributions for decision-making and control. Reward-gradient guided denoising has been recently proposed to generate trajectories that maximize both a differentiable reward function and the likelihood under the data distribution captured by a diffusion model. Reward-gradient guided denoising requires a differentiable reward function fitted to both clean and noised samples, limiting its applicability as a general trajectory optimizer. In this paper, we propose Diffusion-ES, a method that combines gradient-free optimization with trajectory denoising to optimize black-box non-differentiable objectives while staying in the data manifold. Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function. It mutates high-scoring trajecto-ries using a truncated diffusion process that applies a small number of noising and denoising steps, allowing for much more efficient exploration of the solution space. We show that Diffusion-Ex achieves state-of-the-art performance on nuPlan, an established closed-loop planning benchmark for autonomous driving. Diffusion-ES outperforms existing sampling-based planners, reactive deterministic or diffusion-based policies, and reward-gradient guidance. Additionally, we show that unlike prior guidance methods, our method can optimize non-differentiable language-shaped reward functions generated by few-shot LLM prompting. When guided by a human teacher that issues instructions to follow, our method can generate novel, highly complex behaviors, such as aggressive lane weaving, which are not present in the training data. This allows us to solve the hardest nuPlan scenarios which are beyond the capabilities of existing tra-jectory optimization methods and driving policies.11Project page: diffusion-es.github.io

Scene Graph Generators (SGGs) are models that, given an image, build a directed graph where each edge represents a predicted subject predicate object triplet. Most SGGs silently exploit datasets' bias on relationships' context, i.e. its subject and object, to improve recall and neglect spatial and visual evidence, e.g. having seen a glut of data for person wearing shirt, they are overconfident that every person is wearing every shirt. Such imprecise predictions are mainly ascribed to the lack of negative examples for most relationships, which obstructs models from meaningfully learning predicates, even those that have ample positive examples. We first present an indepth investigation of the context bias issue to showcase that all examined state-of-the-art SGGs share the above vulnerabilities. In response, we propose a semi-supervised scheme that forces predicted triplets to be grounded consistently back to the image, in a closed-loop manner. The developed spatial common sense can be then distilled to a student SGG and substantially enhance its spatial reasoning ability. This Grounding Consistency Distillation (GCD) approach is model-agnostic and benefits from the superfluous unlabeled samples to retain the valuable context information and avert memorization of annotations. Furthermore, we demonstrate that current metrics disregard unlabeled samples, rendering themselves incapable of reflecting context bias, then we mine and incorporate during evaluation hard-negatives to reformulate precision as a reliable metric. Extensive experimental comparisons exhibit large quantitative - up to 70% relative precision boost on VG200 dataset - and qualitative improvements to prove the significance of our GCD method and our metrics towards refocusing graph generation as a core aspect of scene understanding. Code available at https://github.com/deeplab-ai/grounding-consistent-vrd.

Detecting visual relationships, i.e.triplets, has been a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embeddings, where the visual features of each branch are driven by a multimodal attentional mechanism that exploits spatio-linguistic similarities in a low-dimensional space. We present a variety of experiments comparing against all related approaches in the literature, as well as by re-implementing and fine-tuning several of them. Results on the commonly employed VRD dataset [1] show that the proposed method clearly outperforms all others, while we also justify our claims both quantitatively and qualitatively.

We address the task of Visual Relationship Detection, i.e. the detection oftriplets in an image, introducing Multimodal Attentional Translation Embeddings (ICIP 2019 paper, id 3642). Motivated by the need of visualization and interpretation of the results, as well as the lack of other tools for online predictions on this task, we design and implement the first architecture for live inference of visual relationships on video streams and wild images, including research and engineering extensions, ablation models and a lightweight CPU-version. The code is available at https://bitbucket.org/deeplabai/vrd.