Circles represent the contact points in the human demo / grasping points for robot manipulation. Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effective in generalizing across different object categories. DenseMatcher first computes vertex features by projecting multiview 2D features onto meshes and refining them with a 3D network, and subsequently finds dense correspondences with the obtained features using functional map. In addition, we craft the first 3D matching dataset that contains colored object meshes across diverse categories. In our experiments, we show that DenseMatcher significantly outperforms prior 3D matching baselines by 43.5%. We demonstrate the downstream effectiveness of DenseMatcher in (i) robotic manipulation, where it achieves crossinstance and cross-category generalization on long-horizon complex manipulation tasks from observing only one demo; (ii) zero-shot color mapping between digital assets, where appearance can be transferred between different objects with relatable geometry. Correspondence plays a pivotal role in robotics Wang (2019). For instance, in robotic assembly, it is necessary to determine the corresponding parts between the target and source objects.

The advancements in automatic text-to-3D generation have been remarkable. Most existing methods use pre-trained text-to-image diffusion models to optimize 3D representations like Neural Radiance Fields (NeRFs) via latent-space denoising score matching. Yet, these methods often result in artifacts and inconsistencies across different views due to their suboptimal optimization approaches and limited understanding of 3D geometry. Moreover, the inherent constraints of NeRFs in rendering crisp geometry and stable textures usually lead to a two-stage optimization to attain high-resolution details. This work proposes holistic sampling and smoothing approaches to achieve high-quality text-to-3D generation, all in a single-stage optimization. We compute denoising scores in the text-to-image diffusion model's latent and image spaces. Instead of randomly sampling timesteps (also referred to as noise levels in denoising score matching), we introduce a novel timestep annealing approach that progressively reduces the sampled timestep throughout optimization. To generate high-quality renderings in a single-stage optimization, we propose regularization for the variance of z-coordinates along NeRF rays. To address texture flickering issues in NeRFs, we introduce a kernel smoothing technique that refines importance sampling weights coarse-to-fine, ensuring accurate and thorough sampling in high-density regions. Extensive experiments demonstrate the superiority of our method over previous approaches, enabling the generation of highly detailed and view-consistent 3D assets through a single-stage training process.

Imagine you are savoring tea in a peaceful Zen garden: a robot sees your empty cup and starts pouring, hears the increase of the sound pitch as the water level rises in the cup, and feels with its fingers around the handle of the teapot to tell how much tea is left and control the pouring speed. For both humans and robots, multisensory perception with vision, audio, and touch plays a crucial role in everyday tasks: vision reliably captures the global setup, audio sends immediate alerts even for occluded events, and touch provides precise local geometry of objects that reveal their status. Though exciting progress has been made on teaching robots to tackle various tasks [1, 2, 3, 4, 5], limited prior work has combined multiple sensory modalities for robot learning. There have been some recent attempts that use audio [6, 7, 8, 9] or touch [10, 11, 12, 13, 14] in conjunction with vision for robot perception, but no prior work has simultaneously incorporated visual, acoustic, and tactile signals--three principal sensory modalities, and study their respective roles on challenging multisensory robotic manipulation tasks. We aim to demonstrate the benefit of fusing multiple sensory modalities for solving complex robotic manipulation tasks, and to provide an in-depth study of the characteristics of each modality and how they complement each other.