12 accepted papers
We introduce a sequence-conditioned critic for Soft Actor--Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets. Unlike prior approaches that (i) score state--action pairs in isolation or (ii) rely on actor-side action chunking to handl…
As an emerging novel view synthesis approach, 3D Gaussian Splatting (3DGS) demonstrates fast training/rendering with superior visual quality. The two tasks of 3DGS, Gaussian creation and view rendering, are typically separated over time or devices, and thus storage/transmission and finally compressi…
This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajec…
We propose Concavity-induced Distance (CID) as a novel way to measure the dissimilarity between a pair of points in an unoriented point cloud. CID indicates the likelihood of two points or two sets of points belonging to different convex parts of an underlying shape represented as a point cloud. Aft
With the advent of new 3D scanning technologies, point clouds have become a crucial way to depict real and virtual objects/scenes. Point clouds represent the continuous sur-faces of underlying object/scene through a collection (usually millions) of discrete, irregular, and often sparsely distributed…
Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements…
Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we…
Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenge…
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neig…
To reduce storage and computational cost for processing and visualizing large-scale 3D point clouds, an efficient resampling strategy is needed to select a representative subset of 3D points that can preserve contours in the original 3D point cloud. We tackle this problem by using graph-based techni…
Learning graph topology from data is challenging. Previous work leads to learning graphs on which the graph signals used for training are smooth. In this paper, we propose an optimization framework for learning multiple graphs, each associated to a class of signals, such that representation of signa…
Moving object segmentation in video has uses in many applications and is a particularly challenging task when the video is acquired by a moving camera. Typical approaches that rely on principal component analysis (PCA) tend to extract scattered sparse components of the moving objects and generally f…