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Found 12 papers, 8 papers with code
3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
3D Diffuser Actor sets a new state-of-the-art on RLBench with an absolute performance gain of 18. 1% over the current SOTA on a multi-view setup and an absolute gain of 13. 1% on a single-view setup.
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
ODIN: A Single Model for 2D and 3D Segmentation
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.
Ranked #1 on
3D Instance Segmentation
on ScanNet200
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning.
Ranked #7 on
Robot Manipulation
on RLBench
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement
Language is compositional; an instruction can express multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange.
Analogy-Forming Transformers for Few-Shot 3D Parsing
We present Analogical Networks, a model that encodes domain knowledge explicitly, in a collection of structured labelled 3D scenes, in addition to implicitly, as model parameters, and segments 3D object scenes with analogical reasoning: instead of mapping a scene to part segments directly, our model first retrieves related scenes from memory and their corresponding part structures, and then predicts analogous part structures for the input scene, via an end-to-end learnable modulation mechanism.
Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds
We propose a language grounding model that attends on the referential utterance and on the object proposal pool computed from a pre-trained detector to decode referenced objects with a detection head, without selecting them from the pool.
Language Modulated Detection and Detection Modulated Language Grounding in 2D and 3D Scenes
Object detectors are typically trained on a fixed vocabulary of objects and attributes that is often too restrictive for open-domain language grounding, where the language utterance may refer to visual entities in various levels of abstraction, such as a cat, the leg of a cat, or the stain on the front leg of the chair.
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection
Scene Graph Generators (SGGs) are models that, given an image, build a directed graph where each edge represents a predicted subject predicate object triplet.
Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation
Inherent morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping.
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection
Detecting visual relationships, i. e. <Subject, Predicate, Object> triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch.
