CVPR 2024 Papers

This paper presents a novel category agnostic model for visual rearrangement task which can help an embodied agent to physically recover the shuffled scene configuration without any category concepts to the goal configuration. Previous methods usually follow a similar architecture completing the rearrangement task by aligning the scene changes of the goal and shuffled configuration according to the semantic scene graphs. However constructing scene graphs requires the inference of category labels which not only causes the accuracy drop of the entire task but also limits the application in real world scenario. In this paper we delve deep into the essence of visual rearrangement task and focus on the two most essential issues scene change detection and scene change matching. We utilize the movement and the protrusion of point cloud to accurately identify the scene changes and match these changes depending on the similarity of category agnostic appearance feature. Moreover to assist the agent to explore the environment more efficiently and comprehensively we propose a closer-aligned-retrace exploration policy aiming to observe more details of the scene at a closer distance. We conduct extensive experiments on AI2THOR Rearrangement Challenge based on RoomR dataset and a new multi-room multi-instance dataset MrMiR collected by us. The experimental results demonstrate the effectiveness of our proposed method.

Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds. Meanwhile diffusion models have shown appealing performance in generating high-quality images from random noise with high indeterminacy through step-by-step denoising. Inspired by their denoising capability we propose a novel diffusion-based framework (6D-Diff) to handle the noise and indeterminacy in object pose estimation for better performance. In our framework to establish accurate 2D-3D correspondence we formulate 2D keypoints detection as a reverse diffusion (denoising) process. To facilitate such a denoising process we design a Mixture-of-Cauchy-based forward diffusion process and condition the reverse process on the object appearance features. Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of our framework.

Constructing photo-realistic Free-Viewpoint Videos (FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advancements achieved by current neural rendering techniques these methods generally require complete video sequences for offline training and are not capable of real-time rendering. To address these constraints we introduce 3DGStream a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real-time rendering at 200 FPS. Specifically we utilize 3D Gaussians (3DGs) to represent the scene. Instead of the naive approach of directly optimizing 3DGs per-frame we employ a compact Neural Transformation Cache (NTC) to model the translations and rotations of 3DGs markedly reducing the training time and storage required for each FVV frame. Furthermore we propose an adaptive 3DG addition strategy to handle emerging objects in dynamic scenes. Experiments demonstrate that 3DGStream achieves competitive performance in terms of rendering speed image quality training time and model storage when compared with state-of-the-art methods.